Function Approximation Approach Research Articles

On stability and H∞ control synthesis of sampled‐data systems: A multiple convex function approximation approach

AbstractThis article deals with the stability and control synthesis for a class of linear sampled‐data systems. First, a novel integral inequality with the cubic term of integral interval length instead of reciprocal one of integral interval length is constructed by non‐orthogonal polynomials. This can make full use of slack matrix variables and additional information about sawtooth structural sampling pattern. Then, on the basis of the constructed integral inequality, a cubic‐term‐dependent discontinuous exponent Lyapunov–Krasovskii Functional (LKF) is developed containing additional information about system states to design an sampled‐data state feedback controller. To better estimate the upper bound of the derivative of LKF, a kind of multiple convex function approximation approach is developed, which can be used to deal with the two‐variable polynomial negative definite issue by dividing the variable interval into multiple subintervals. By the constructed integral inequality and multiple convex function approximation approach, sufficient conditions with less conservatism are deduced for the feasibility of sampled‐data state feedback controller. Moreover, by the inner convex approximation solution technique and proposed iterative algorithm, the bilinear matrix inequalities can be transformed into linear matrix ones, which can be easily tackled, to obtain the desired sampled‐data controller gain. Finally, by comparing with existing results, the advantages and superiority of our proposed approaches can be confirmed via two numerical examples.

A data-driven implicit deep adaptive neuro-fuzzy inference system capable of manifold learning for function approximation

Fuzzy Neural Networks (FNN) have the ability of decision-making based on constructing semi-ellipsoidal clusters in the input space as the antecedent parts of their fuzzy rules. To determine the output value for each input instance, FNNs consider its membership degree to different sub-regions of the input space. However, forming such meaningful sub-regions is not possible in all applications due to the nonlinear interactions among input variables and their low information gain. Indeed, the samples could be distributed on a manifold in the input space. Therefore, to cover the input space, we need lots of rules, each representing a small region of input space. This issue decreases the generalization ability of the model along with its explainability. Consequently, to efficiently form fuzzy rules, first, it is necessary to unfold the manifold by mapping the samples to an appropriate embedding space. Next, the fuzzy rules in the form of semi-ellipsoidal regions should be constructed in this extracted feature space. Deep Fuzzy Neural Networks address this problem by representation learning through stacking multiple cascade mapping layers. In this paper, we propose a novel approach for nonlinear function approximation and time-series prediction problems, based on using the kernel trick to implicitly learn the mapping function to the new feature space. Moreover, to initialize the fuzzy rules, a KNN-based method using the kernel trick is proposed. A hierarchical Levenberg–Marquardt approach is applied to learn the model’s parameters. The performance and structure of the proposed method are studied and compared with some other relevant methods in synthetic and real-world benchmarks. Based on these experiments, the proposed method has the best performance with the most parsimonious architecture. According to these experiments, the test RMSE of the proposed method is 0.002 for Mc-Glass chaotic time-series prediction, 0.015 for a Nonlinear dynamic system identification, 0.0345 for Box–Jenkins nonlinear system identification, 0.0609 for Fuel consumption prediction of automobiles, 10.24 for Sydney stock price tracking, and 0.595 for California housing.

Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics

Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory policies must be determined for multiple locations in the network, and transportation of hydrogen from the production location to customers must be scheduled. At the same time, production patterns of hydrogen are intermittent, which affects the possibilities to realize the planned transportation and inventory levels. To provide policies for efficient transportation and storage of hydrogen, this paper proposes a parameterized cost function approximation approach to the stochastic cyclic inventory routing problem. Firstly, our approach includes a parameterized mixed integer programming (MIP) model which yields fixed and repetitive schedules for vehicle transportation of hydrogen. Secondly, buying and selling decisions in case of underproduction or overproduction are optimized further via a Markov decision process (MDP) model, taking into account the uncertainties in production and demand quantities. To jointly optimize the parameterized MIP and the MDP model, our approach includes an algorithm that searches the parameter space by iteratively solving the MIP and MDP models. We conduct computational experiments to validate our model in various problem settings and show that it provides near-optimal solutions. Moreover, we test our approach on an expert-reviewed case study at two hydrogen production locations in the Netherlands. We offer insights for the stakeholders in the region and analyze the impact of various problem elements in these case studies. Funding: This project received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under [Grant Agreement 875090]. The Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme, Hydrogen Europe and Hydrogen Europe Research. A. H. Schrotenboer received support from the Dutch Science Foundation (Nederlandse Organisatie voor Wetenschappelijk Onderzoek; NWO) through [Grant VI.Veni.211E.043].

Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning

Monoclonal antibodies (mAbs), crucial in medicine and biopharmaceuticals, require optimized large-scale production to meet high clinical dosage demands. Most of the processes for industrial mAb production rely on fed-batch operations, resulting in significant downtime. Transitioning to fully continuous and integrated processes holds the potential to boost product yield, enhance quality, and reduce storage costs for intermediate products. The integrated continuous mAb production process can be divided into the upstream and downstream processes. One crucial aspect that ensures the continuity of the integrated process is the switching of the capture columns, which are typically chromatography columns operated in a fed-batch manner downstream. Due to the discrete nature of the switching operation, advanced process control algorithms such as economic MPC (EMPC) are computationally difficult to implement. This is because an integer nonlinear program (INLP) needs to be solved online at each sampling time.This paper introduces two computationally-efficient approaches for EMPC implementation, namely, a sigmoid function approximation approach and a rectified linear unit (ReLU) approximation approach. It also explores the application of deep reinforcement learning (DRL). These three methods are compared to the traditional switching approach which is based on a 1 % product breakthrough rule and which involves no optimization.

SFQEDtoolkit: A high-performance library for the accurate modeling of strong-field QED processes in PIC and Monte Carlo codes

Strong-field quantum electrodynamics (SFQED) processes are central in determining the dynamics of particles and plasmas in extreme electromagnetic fields such as those present in the vicinity of compact astrophysical objects or generated with ultraintense lasers. SFQEDtoolkit is an open source library designed to allow users for a straightforward implementation of SFQED processes in existing particle-in-cell (PIC) and Monte Carlo codes. Through advanced function approximation techniques, high-energy photon emission and electron-positron pair creation probability rates and energy distributions are calculated within the locally-constant-field approximation (LCFA) as well as with more advanced models [Phys. Rev. A 99, 022125 (2019)]. SFQEDtoolkit is designed to provide users with high-performance and high-accuracy, and neat examples showing its usage are provided. In the near future, SFQEDtoolkit will be enriched to model the angular distribution of the generated particles, i.e., beyond the commonly employed collinear emission approximation, as well as to model spin and polarization dependent SFQED processes. Notably, the generality and flexibility of the presented function approximation approach makes it suitable to be employed in other areas of physics, chemistry and computer science. Program summaryProgram Title: SFQEDtoolkit (version 1.0)CPC Library link to program files:https://doi.org/10.17632/j7mhj3cg3m.1Developer's repository link:https://github.com/QuantumPlasma/SFQEDtoolkitLicensing provisions: GPLv3Programming language: C++, FortranNature of problem: The accurate simulation of strong-field QED processes in the interaction of particles or plasmas with strong electromagnetic fields is critical for present and future experiments with ultraintense lasers as well as to unveil the dynamics around compact astrophysical objects. State-of-the-art PIC and Monte Carlo codes implement strong-field QED processes by means of lookup tables, where the approximated function is evaluated on a fixed set of points. This typically results in numerical artifacts such as stairlike structures in the approximated function, and in a reduced efficiency when large size lookup tables are necessary. An easily implementable open-source accurate and efficient simulation tool is required to address problems at the high-intensity frontier of plasma physics and astrophysics.Solution method: Strong-field QED process rates and particle distributions are calculated by leveraging advanced approximation methods based on variable and function transformation, asymptotic expansions as well as Chebyshev polynomial expansions. This provides a numerically-stable and efficient approach that allows us to overcome all limitations of existing methods based on standard lookup tables.

Spatiotemporal decomposed dispatch of integrated electricity-gas system via stochastic dual dynamic programming-based value function approximation

This paper proposes a novel stochastic dual dynamic programming-based value function approximation approach for the spatiotemporal decomposed dispatch of integrated electricity-gas systems with uncertainties. Stochastic dual dynamic programming is employed to decompose the optimal dispatch problem into several subproblems in both spatial and temporal dimensions. Then, Benders cuts are used in the real-time dispatch process to describe the interaction among these subproblems, according to which each subsystem can make decentralized and real-time decisions. In this way, both the information privacy and decision independence of each subsystem can be guaranteed, while the future variability of wind power and loads can be firmed. Moreover, the historical information can be used to obtain the Benders cuts offline, which helps to omit the time-consuming iterations in the real-time decision stage, while the near-optimal solutions can still be obtained. The effectiveness of the proposed approach is verified by case studies on a 4-bus-4-node system and a 118-bus-20-node system. Numerical results demonstrate that the proposed method reduces the average error of the operation cost by 1 order of magnitude compared with the traditional real-time methods. Moreover, the computational time is significantly reduced by up to 4 orders of magnitude compared with the traditional decentralized algorithms.

Efficiency in the serverless cloud paradigm: A survey on the reusing and approximation aspects

SummaryServerless computing along with Function‐as‐a‐Service (FaaS) is forming a new computing paradigm that is anticipated to found the next generation of cloud systems. The popularity of this paradigm is due to offering a highly transparent infrastructure that enables user applications to scale in the granularity of their functions. Since these often small and single‐purpose functions are managed on shared computing resources behind the scene, a great potential for computational reuse and approximate computing emerges that if unleashed, can remarkably improve the efficiency of serverless cloud systems—both from the user's QoS and system's (energy consumption and incurred cost) perspectives. Accordingly, the goal of this survey study is to, first, unfold the internal mechanics of serverless computing and, second, explore the scope for efficiency within this paradigm via studying function reuse and approximation approaches and discussing the pros and cons of each one. Next, we outline potential future research directions within this paradigm that can either unlock new use cases or make the paradigm more efficient.

Improved binary particle swarm optimization for the deterministic security-constrained transmission network expansion planning problem

- This paper proposes an improved version of Binary Particle Swarm Optimization (BPSO) to solve the security-constrained transmission network expansion planning (TNEP) problem using the DC network model. Some modifications, such as superior population configurations, are made to improve the efficiency of the BPSO by adapting it to the needs of the TNEP to obtain better results. A parallel implementation of the improved BPSO reduces execution times. Transmission losses are included using a quadratic function and piecewise linear approximation approach. Considering transmission losses in the long-term planning horizon can avoid underinvestment in network capacity and result in a different network expansion plan. The proposed approach is validated with numerical simulations of the classic Garver system, and its performance is tested on the IEEE 24-bus and 118-bus systems for a ten-year planning horizon. The simulation results are compared to those obtained from classic BPSO to verify the efficiency of the improved version of the BPSO.

Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services

The availability of various services for individual mobility is increasing, especially in urban areas. Dynamic ride-hailing services address these aspects and are gaining market share with providers such as MOIA, UberX Share, Sprinti or BerlKönig. To be able to offer competitive pricing for such a service and at the same time provide a high service quality (e.g. fast response times), effective capacity management is needed. In order to reach this goal, two challenges have to be met by the service provider. On the one hand, a proper demand control has to be installed, which optimizes the responses to transportation requests from customers. On the other hand, suitable fleet control needs to be set in place to optimize the route of the fleet so that the demand can be met. Papers in the literature do solve both but typically focus on one of these two challenges. As an example, value function approximation (VFA) can be used to learn a service offering decision while anticipating future incoming requests. A typical example of a routing-focused method is the multiple scenario approach (MSA) creating a routing which anticipates future requests using a sampling method. In this paper, we combine VFA and MSA to address the two challenges in an effective way. The resulting method is called anticipatory-routing-and-service-offering (ARS). We find that the combined method significantly outperforms the individual components, improving not only the total reward but also the accepted requests. It is found that this performance is particularly high with a heavy workload and thus resources are relatively scarce. We analyse how and under which conditions the components together or individually are particularly important.

A Function Approximation Approach for Parametric Optimization

We present a novel approach for approximating the primal and dual parameter-dependent solution functions of parametric optimization problems. We start with an equation reformulation of the first-order necessary optimality conditions. Then, we replace the primal and dual solutions with some approximating functions and find for some test parameters optimal coefficients as solution of a single nonlinear least-squares problem. Under mild assumptions it can be shown that stationary points are global minima and that the function approximations interpolate the solution functions at all test parameters. Further, we have a cheap function evaluation criterion to estimate the approximation error. Finally, we present some preliminary numerical results showing the viability of our approach.

Adaptive Control of Wire-Borne Underactuated Brachiating Robots Using Control Lyapunov and Barrier Functions

Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics. We present and experimentally implement an online adaptive controller for a wire-borne brachiating robot swinging on a vibrating cable. An adaptive function approximation approach is proposed to estimate the unknown dynamics of the flexible cable as an external force applied to the robot. Robust control Lyapunov and barrier functions are designed and incorporated into quadratic programs to synthesize a unified adaptive control framework, which formally guarantees the stability and safety of the brachiating robot in the presence of dynamic uncertainties, actuator constraints, and obstacles in the environment. The stability analysis and derivation of the adaptation law are carried out through a Lyapunov analysis. We demonstrate and validate the proposed control framework using extensive hardware experiments with an underactuated brachiating robot operating on a flexible cable. Simulation results, hardware experiments, and comparisons with a baseline controller show that the proposed quadratic programming-based controller achieves reliable tracking performance and disturbance estimation in the presence of model uncertainties, actuator limits, and safety constraints.

A combined 2-D and 3-D QSAR modeling, molecular docking study, design, and pharmacokinetic profiling of some arylimidamide-azole hybrids as superior L. donovani inhibitors

BackgroundLeishmaniasis is one of the neglected tropical diseases which is prevalent in the tropical regions of the world most especially in Africa. It is caused by the Leishmania species and transmitted to humans majorly through the bite of the female sandfly. This study was carried out in support of the continuous search for new drug molecules effective enough for the treatment of leishmaniasis, and which have very limited side effects. This study was focused on a combined 2-D and 3-D QSAR modeling of thirty-six arylimidamide-azole hybrids, molecular docking study, design, and pharmacokinetic analysis of some selected and newly designed arylimidamide-azole analogs. The density functional theory (DFT) with B3LYP and 6-31G** basis set was employed for the geometry optimization of the various compounds. Genetic function approximation (GFA) and multi-linear regression (MLR) approaches were used for the 2-D QSAR model building, while the fractional factorial design (FFD) and uninformative variable elimination-partial least square (UVEPLS) were employed for building the 3-D QSAR model. Pyridoxal kinase (PdxK) receptor (PDB: 6K91) was the target protein of interest in this study.ResultsThe built 2-D and 3-D QSAR models were found to satisfy the requirement of both internal and external validation tests as follows: 2-D QSAR; R2 = 0.9614, R2adj = 0.9513, Q2cv = 0.9350, R2test = 0.6766 and cRp2 = 0.8779, and for 3-D QSAR (UVEPLS at PC = 5); R2 = 0.9839, Q2LOO = 0.7539 and Q2LTO = 0.7367. The CoMFA steric and electrostatic field contributions were 68.96% and 31.04%, respectively. All the designed analogs showed higher predicted activities than that of the template (36). Also, the new compounds showed higher binding affinities (MolDock scores) than that of the reference drug pentamidine (− 141.793 kcal/mol), with 36e showing the highest negative MolDock score of − 208.595 kcal/mol. Additionally, these newly designed compounds were said to be orally bioavailable (excluding 36f and 36g that violated 2 of the Lipinski’s provisions), with perfect intestinal absorption, less difficult to synthesize, AMES toxicity free, and showed strong interactions with the target.ConclusionsThe newly designed compounds especially 36e have shown a marked pharmacological improvement over the template molecule and are therefore recommended for further practical evaluation as superior pyridoxal kinase inhibitors.

Using the Concept of Fuzzy Logic and Controller System in Interpolation and Functions Approximation

The main aim of this contribution is to present an alternative approach for interpolation and function approximation from a given data. Instead of the traditional interpolation methods we consider and propose a numerical procedure for interpolation using the concept of fuzzy logic and membership functions. The method can be used for interpolating data resulting from physical experiments, engineering, medicine, applied sciences etc. Fuzzification will be applied to the given data according to Mamdani technique and membership functions will be chosen to satisfy the interpolation mathematical condition. The defuzzification process will be implemented to get the crisp values of the interpolation. The procedure will be implemented on the mathematical code MATLAB and its Simulink fuzzy logic features. Finally the applicability and efficiency of the procedure is illustrated by numerical examples.

Adaptive backstepping control design for MEMS gyroscope based on function approximation techniques with input saturation and output constraints

An efficient adaptive backstepping control method is presented in the current study for controlling the micro-electro-mechanical system (MEMS) triaxial gyroscope. In this approach, practical considerations, including output constraints and input saturation, are taken into account. A Barrier Lyapunov Function (BLF) is used so that non-transgression of the constraints is ensured. In the suggested control method, for approximating the lumped uncertainties and addressing the problem of explosion of complexity, Hermite polynomials are used as a simple but effective function approximation approach as a universal approximator. Therefore, the resulting control approach is much simpler with minimal learning parameters. The Lyapunov stability theory is used so that the semiglobally uniformly ultimately boundedness of the closed-loop signals with proper tracking error convergence is guaranteed. According to the results of the comparative simulation, the superiority of this approach is confirmed.

A Function Approximation Approach to the Prediction of Blood Glucose Levels

The problem of real time prediction of blood glucose (BG) levels based on the readings from a continuous glucose monitoring (CGM) device is a problem of great importance in diabetes care, and therefore, has attracted a lot of research in recent years, especially based on machine learning. An accurate prediction with a 30, 60, or 90 min prediction horizon has the potential of saving millions of dollars in emergency care costs. In this paper, we treat the problem as one of function approximation, where the value of the BG level at time t+h (where h the prediction horizon) is considered to be an unknown function of d readings prior to the time t. This unknown function may be supported in particular on some unknown submanifold of the d-dimensional Euclidean space. While manifold learning is classically done in a semi-supervised setting, where the entire data has to be known in advance, we use recent ideas to achieve an accurate function approximation in a supervised setting; i.e., construct a model for the target function. We use the state-of-the-art clinically relevant PRED-EGA grid to evaluate our results, and demonstrate that for a real life dataset, our method performs better than a standard deep network, especially in hypoglycemic and hyperglycemic regimes. One noteworthy aspect of this work is that the training data and test data may come from different distributions.

Neuro‐Fuzzy Kinematic Finite‐Fault Inversion: 1. Methodology

AbstractKinematic finite‐fault source inversions aim at resolving the spatio‐temporal evolution of slip on a fault given ground motion recorded on the Earth's surface. This type of inverse problem is inherently ill posed due to two main factors. First, the number of model parameters is typically greater than the number of independent observed data. Second, small singular values are generated by the discretization of the physical rupture process and amplify the effect of noise in the inversion. As a result, one can find different slip distributions that fit the data equally well. This ill posedness can be mitigated by decreasing the number of model parameters, hence improving their relationship to the observed data. In this study, we propose a fuzzy function approximation approach to describe the spatial slip function. In particular, we use an Adaptive Network‐based Fuzzy Inference System (ANFIS) to find the most adequate discretization for the spatial variation of slip on the fault. The fuzzy basis functions and their respective amplitudes are optimized through hybrid learning. We solve this earthquake source problem in the frequency domain, searching for optimal spatial slip distribution independently for each frequency. The approximated frequency‐dependent spatial slip functions are then used to compute the forward relationship between slip on the fault and ground motion. The method is constrained through Tikhonov regularization, requiring a smooth spatial slip variation. We discuss how the number of model parameters can be decreased, while keeping the inversion stable and achieving an adequate resolution. The proposed inversion method is tested using the SIV1‐benchmark exercise.

Crashworthiness design for multi-cell circumferentially corrugated thin-walled tubes with sub-sections under multiple loading conditions

Multi-cell, multi-corner and adding edge-junctions structures are widely used approaches to enhance the crash characteristic of the thin-walled structures. In this study, the crashworthiness of twenty-one structures combining these three structures is examined under axial and oblique loading angles. The finite element models under axial loading are validated by experimental data from the literature and theoretical approach. In the theoretical approach, removing the corner elements in the inner structure from the theoretical calculation in multi-cell tubes has increased the accuracy. With the validations performed in axial loadings, it is predicted that the finite element model will be accurate also in oblique loadings. On the other hand, rupture strain has not been used in finite element models, which may cause some errors. Crashworthiness performance has improved as the cell number increases under all loading conditions except the C2 tube under 20-degree oblique loading. Also, the sub-sections added to the inner wall corners of the tubes significantly increase the energy absorption capacity. The complex proportion assessment (COPRAS) method and the technique for order of preference by similarity to ideal solution (TOPSIS) are utilized to get the tube with the best crashworthiness performance. The entropy method is used for weighting to avoid human intervention. The best tube varies depending on the weighting and selection method. Finally, the radial basis function (RBF) approximation approach and four multiobjective optimization methods are used to obtain optimum sizes of the C4O tube. The results of the optimizations show that the optimum structure does not differ depending on the optimization method, and the results are very close to each other.

Autonomous Mobility Management for 5G Ultra-Dense HetNets via Reinforcement Learning With Tile Coding Function Approximation

Mobility management is an important feature in modern wireless networks that can provide seamless and ubiquitous connectivity to mobile users. Due to the dense deployment of small cells and heterogeneous network topologies, the traditional handover control method can lead to various mobility-related problems, such as frequent handovers and handover failures. On the other hand, the mobility management’s maintenance and operation cost is also increased due to increasing node density. In this paper, an autonomous mobility management control approach is proposed to increase the mobility robustness of user equipment (UE) mobility and minimize the operational cost of mobility management. The proposed method is based on reinforcement learning, which can autonomously learn an optimal handover control policy by interacting with the environment. The function approximation approach is adopted to allow reinforcement learning to process a large state and action space. A linear function approximator is used to approximate the state-action value function. Finally, the semi-gradient state-action-reward-state-action (Sarsa) method is implemented to update the approximated state-action function and learn the optimal handover control policy. The simulation results show that the proposed method can effectively improve the mobility robustness of UE under different speed ranges. Compared with the conventional reference signal received power (RSRP) based approach, the proposed approach can reduce unnecessary handovers by about 20% and latency by 58%, while achieving near zero handover failure rate, and increasing throughput by 12%.

Command filtering-based adaptive control for chaotic permanent magnet synchronous motors considering practical considerations

In this paper, an efficient adaptive control is designed for chaotic Permanent Magnet Synchronous Motors (PMSMs) with full-state asymmetric time-varying constraints in the input saturation presence. The strategy that is suggested in this work is equipped with the command filtering for addressing the problem of the “explosion of complexity” available in the common backstepping method. In addition, the filtering errors are incorporated into the control design procedure for improving the control system performance. During the control design, the asymmetric barrier Lyapunov functions (BLFs) are employed so that the restriction of state variables in the given intervals is guaranteed. In the suggested control method, for approximating unknown nonlinear functions, the Bessel series is utilized as a simple but effective function approximation approach as a universal approximator. The presented design provides this advantage that by considering practical considerations, a reduced-order observer is also designed so that there is no need to mount the physical sensors to measure the position and the velocity of the chaotic PMSM. The Lyapunov stability theory is used to establish the boundedness of all the closed-loop signals. According to the comparative results obtained with neural networks, the presented control design is able to suppress the chaotic behavior of the PMSM drive system while ensuring an excellent tracking performance.

P.296 Interhemispheric imbalance of motor cortex excitability in affective disorders – a systematic review and meta-analysis

Tuberculosis (TB) is of one the most infectious disease caused by Mycobacterium tuberculosis which remains a serious public health problem. Emergence of multi-drug resistant strains of M. tuberculosis led to development of new and more potent anti-tuberculosis agents. The aim of this study was to correlate the chemical structures of the inhibitory compounds with their experimental activities. In this study, analogs of 2,4-disubstituted quinoline derivatives as potent anti-tubercular agents was subjected to quantitative structure–activity relationship (QSAR) analysis in order to build a QSAR model for predicting the activities of these compounds. In order to build the regression model, Genetic Function Approximation (GFA) and Multi-linear Regression approach were used to predict the activities of inhibitory compounds. Based on the prediction, the best validation model was found to have squared correlation coefficient (R2) of 0.9367, adjusted squared correlation coefficient (R2 adj) value of 0.9223 and cross validation coefficient (Qcv2) value of 0.8752. The chosen model was subjected to external validations and the model was found to have (R2 test) of 0.8215 and Y-randomization Coefficient (cRp2) of 0.6633. The proposed QSAR model provides a valuable approach for designing more potent anti-tubercular agents.