Soft Computation Research Articles

Application‐Based Review of Soft Computational Methods to Enhance Industrial Practices Abetted by the Patent Landscape Analysis

ABSTRACTSoft computing is a collective methodology that touches all engineering and technology fields owing to its easiness in solving various problems while comparing the conventional methods. Many analytical methods are taken over by this soft computing technique and resolve it accurately and the soft computing has given a paradigm shift. The flexibility in soft computing results in swift knowledge acquisition processing and the information supply renders versatile and affordable technological system. Besides, the accuracy with which the soft computing technique predicts the parameters has transformed the industrial productivity to a whole new level. The interest of this article focuses on versatile applications of SC methods to forecast the technological changes which intend to reorient the progress of various industries, and this is ascertained by a patent landscape analysis. The patent landscape revealed the players who are in the segment consistently and this also provides how this field moves on in the future and who could be a dominant country for a specific technology. Alongside, the accuracy of the soft computing method for a particular practice has also been mentioned indicating the feasibility of the technique. The novel part of this article lies in patent landscape analysis compared with the other data while the other part is the discussion of application of computational techniques to various industrial practices. The progress of various engineering applications integrating them with the patent landscape analysis must be envisaged for a better understanding of the future of all these applications resulting in an improved productivity.

Prediction of saturation exponent for subsurface oil and gas reservoirs using soft computing methods

The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean squared error, root mean squared error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous.

Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network

The pumping station is one of the critical parts of the hydraulic structure in China. Traditional forecasting methods are limited in accuracy, time-consuming, and high cost, resulting in limited data availability. Therefore, simulation model analysis based on soft computation is a realistic and valuable alternative. This article intends to use the BP neural network to predict the safe operation status of pump stations and optimize the initial threshold and weight information of the BP network using the sparrow search algorithm (SSA) to improve the accuracy and generalization ability of the model. In addition, to more accurately reflect the correlation between various influencing factors and the safe operation status of the pumping station, the entropy weight method and the analytic hierarchy process were used to obtain the comprehensive weights of each main influencing factor. The experimental results show that the SSA-BP model can accurately predict the safe operation status of pumping stations, and compared with other traditional models, the SSA-BP model has better convergence and higher accuracy. This model provides a new approach for predicting the safe operation of pumping stations and has particular reference significance for predicting the safe operation of other pumping stations.

A stagnation point flow of cross nanofluid flow: Levenberg Marquardt backpropagation computational approach

AbstractIn the present article, the intelligence based numerical investigation by Levenberg–Marquardt algorithm with Back‐propagated Artificial Neural Networks (LMA–BANN) is exploited to analyze the nonlinear radiative stagnation point flow of cross nanofluid system (NRS‐CNFS) past a stretching surface. The designed NRS‐CNFS, initially represented by system of partial differential equations (PDEs), is converted into system of non‐linear ODEs through the applicability of mathematical conversion analysis. The desired reference solution in the form of a dataset for LMA–BANN is achieved from the Adam method for NRS‐CNFS's different scenarios by varying the magnetic parameter (Ha), Prandlt number (Pr), thermal radiation parameter(R), Eckert number (Ec), thermophoresis diffusion coefficients (Nt), and non‐dimensional activation energy (E). The obtained results are construed for the NRS‐CNFS through the performances of the testing, validation, and training. In addition, the comparison is provided using the LMA ‐BANN analysis, which is validated in terms of regression, fitness estimations, and histograms along with MSE performances. Hence, the article provides an impeccable innovative approach to problem‐solving by implementing a soft computation paradigm. It promotes the use of an effective and dependable alternative framework that is based on soft computing environments and emphasizes descriptive analysis to successfully handle the problems brought on by varied physical features.

Soft Computing-Based Driver Modeling for Automatic Parking of Articulated Heavy Vehicles

<div>Parking an articulated vehicle is a challenging task that requires skill, experience, and visibility from the driver. An automatic parking system for articulated vehicles can make this task easier and more efficient. This article proposes a novel method that finds an optimal path and controls the vehicle with an innovative method while considering its kinematics and environmental constraints and attempts to mathematically explain the behavior of a driver who can perform a complex scenario, called the articulated vehicle park maneuver, without falling into the jackknifing phenomena. In other words, the proposed method models how drivers park articulated vehicles in difficult situations, using different sub-scenarios and mathematical models. It also uses soft computing methods: the ANFIS-FCM, because this method has proven to be a powerful tool for managing uncertain and incomplete data in learning and inference tasks, such as learning from simulations, handling uncertainty, and capturing expert parking expertise. The results obtained from the proposed method show that the use of a soft computation method significantly reduces the cumulative errors: errors resulting from summing up each sub-maneuver. Of course, the main source of these errors is related to starting from the random point that exists at the beginning of the predefined complex scenario. This implies that our method can effectively handle the uncertainty and variability of parking scenarios.</div>

Neuro-computing for third-grade nanomaterial flow under impacts of activation energy and mixed convection along rotating disk

This paper examines the activation energy influence in third-grade nanoparticle flow model (TG-NPFM), which is nonlinear mixed convective flow over a spinning disk under the influence of heat sink/source as well as viscous dissipation by utilizing Bayesian Regulation Method with backpropagated Artificial Neural Networks (BRM-BPANN). Nonlinear thermal radiation is also involved in the considered flow dynamics to obtain the approximated numerical solutions. The nonlinear PDEs of TG-NPFM are then transformed into nonlinear ODEs by implementing the corresponding transformation. We solved these ODEs by Optimal Homotopy Analysis Method (OHAM) to explain the dataset used as a reference for BRM-BPANN for different scenarios of TG-NPFM. This reference dataset is then exported to MATLAB to compute the results. The outcomes of TG-NPFM are figured by adopting the procedures of testing, validation and training. Moreover, approximated solution is compared with standard solution and the efficacy examination of TG-NPFM is authenticated by the studies of MSE, error histogram and regression plots. These soft computation frameworks provide incentive to use an efficient and dependable alternative paradigm built on soft computing environments to solve problems by doing a descriptive analysis to mitigate the impacts of different physical features. It is a new implementation of intelligent computational system of artificial intelligence introduced by incorporating the solver BRM-BPANN for interpreting the TG-NPFM. The absolute error values lie between 10[Formula: see text] to 10[Formula: see text], 10[Formula: see text] to 10[Formula: see text], 10[Formula: see text] to 10[Formula: see text], 10[Formula: see text] to 10[Formula: see text], 10[Formula: see text] to 10[Formula: see text], 10[Formula: see text] to 10[Formula: see text], 10[Formula: see text] to 10[Formula: see text] and 10[Formula: see text] to 10[Formula: see text], which show the reliability and accuracy of the technique. The convergence and precision of the algorithm can easily be seen through the results of performance, training state and fitness plot, along with the regression value of [Formula: see text].

Time-efficient soft computational approach on vibration response of carbon-enriched drive shafts of super-fast race cars

Carbon-enriched nanocomposite drive shafts have received considerable attention in super-fast race cars because of their superb material and mechanical properties. thus, for the first time, vibration behavior of drive shafts enriched by three conventional distribution patterns of carbon nanotubes (CNT) in the polymer matrix are explored and reported. A new form in the framework of First-order Timoshenko beam theory is employed. The elasticity moduli of the CNT enriched nanocomposite material are approximated through a refined model of rule of mixtures. Hamilton’s variational principle serves as a way to determine the beam’s equations of motion. Differential quadrature approach (DQA) is employed to predict the system’s frequency for some conditions labeled as the training information. A new soft computation approach based on deep-learning methods employs the training information to adjust a predictor mechanism for obtaining the system’s vibration for any other conditions. This novel methodology provides this opportunity to considerably reduce the computational effort. Validity of the mentioned solution is verified through a comparative scrutinization by low discrepancy between its results and those of the published studies. Importance of these agility in computations would be discerned in cataloging process of the manufactured drive shafts in car industry.

Employee Productivity Assessment Using Fuzzy Inference System

The success of an organization hinges upon the effective utilization of its human resources, which serves as a crucial developmental factor and competitive advantage, and sets the organization apart from others. Evaluating staff productivity involves considering various dimensions, notably structural, behavioral, and circumferential factors. These factors collectively form a three-pronged model that comprehensively encompasses the facets of an organization. However, assessing the productivity of employees poses challenges, due to the inherent complexity of the humanities domain. Fuzzy logic offers a sound approach to address this issue, employing its rationale and leveraging a fuzzy inference system (FIS) as a sophisticated toolbox for measuring productivity. Fuzzy inference systems enhance the flexibility, speed, and adaptability in soft computation. Likewise, their applications, integration, hybridization, and adaptation are also introduced. They also provide an alternative solution to deal with imprecise data. In this study, we endeavored to identify and measure the productivity of human resources within a case study, by developing an alternative framework known as an FIS. Our findings provided evidence to support the validity of the alternative approach. Thus, the utilized approach for assessing employee productivity may provide managers and businesses with a more realistic asset.

Dynamical behavior of analytical soliton solutions, bifurcation analysis, and quasi-periodic solution to the (2+1)-dimensional Konopelchenko–Dubrovsky (KD) system

Nonlinear evolution equations (NLEEs) are extensively used to establish the elementary propositions of natural circumstances. In this work, we study the Konopelchenko–Dubrovsky (KD) equation which depicts non-linear waves in mathematical physics with weak dispersion. The considered model is investigated using the combination of generalized exponential rational function (GERF) method and dynamical system method. The GERF method is utilized to generate closed-form invariant solutions to the (2+1)-dimensional KD model in terms of trigonometric, hyperbolic, and exponential forms with the assistance of symbolic computations. Moreover, 3D, 2D combined line graph and their contour graphics are displayed to depict the behavior of obtained solitary wave solutions. The model is observed to have multiple soliton profiles, kink-wave profiles, and periodic oscillating nonlinear waves. These generated solutions have never been published in the literature. All the newly generated soliton solutions are checked by putting them back into the associated system with the soft computation via Wolfram Mathematica. Moreover, the system is converted into a planer dynamical system using a certain transformation and the analysis of bifurcation is examined. Furthermore, the quasi-periodic solution is investigated numerically for the perturbed system by inserting definite periodic forces into the considered model. With regard to the parameter of the perturbed model, two-dimensional and three-dimensional phase portraits are plotted.

Machine Learning Based Design of Pattern Reconfigurable Antenna

In this study, a Machine Learning (ML) is implemented to soft computation of the Reconfigurable Horn Bowtie Dumbbell (RHBD) antenna at operating frequency range from 26 GHz to 29.5 GHz for 5G applications. An adaptive learning rate approach is used to build a ML model on a 5-layer system utilizing a simulated database of 180 RHBD antennas. In the training stage of a hybrid method that combines the advantages of particle swarm optimization (PSO) with a modified version of the gravitational search algorithm (MGSA), the architecture frame and hyper-parameters of the ML model are optimized. A precise electromagnetic analysis platform is used to simulate 180 RHBD antennas with varying geometrical properties in terms of the resonant frequency in order to create the database for training and testing the model. The ML model is tested and validated using a fabricated RHBD antenna operating at 27.5 GHz. Then, three PIN diodes are placed in the gaps of the reflectors located at the back of the antenna, and by changing the state of these PIN diodes, it can be noticed that they have a significant and direct effect on the radiation pattern, as they are able to change the beamwidth from 10.7° to 156.2°. The suggested antenna makes it easier to create dynamic radiation patterns that may be utilized to reconfigure the coverage area as required in accordance with the spatial-temporal user and traffic variations in high mobility environments.

Frequency control of the islanded microgrid including energy storage using soft computing

Today, with the increasing penetration of microgrids, the degree of complexity and non-linearity of power systems has increased, causing conventional and inflexible controllers not to perform well in a wide range of operating points. In this paper, a self-tuning proportional-integral (PI)-controller based on a soft computation of a combination of genetic algorithm (GA) and artificial neural network (ANN). The GA-ANN is used to control the frequency of a microgrid in an island mode to automatically adjust and optimize the coefficients of a PI-controller. The proposed PI-controller is located in the frequency control secondary loop of an island microgrid. Since the ANN is a local search algorithm and can be located in local minimum points and on the other hand improving its performance requires a lot of training data. The ANN parameters are optimized using the GA algorithm's proposed controller. Train ANN online to adapt to the system and change the PI-control coefficients without a lot of training data, in addition to avoiding being in the local minimum points.The microgrid tested included various distributed generation units including battery energy storage that tried to create a more realistic frequency response for the microgrid by considering nonlinear factors on the model of these resources. Finally, the simulation results with different perturbations indicate the proper performance of the proposed controller.

Exploring the fractional Hirota Maccari system for its soliton solutions via impressive analytical strategies

In the field of marine engineering, the characteristics of wave propagation play an imperative character. In many geographical regions, the key source of environmental effects on artificial floating or stationary structures or seashores is waves. This article deals with the Hirota Maccari system (HMs) via a new fractional derivative operator known as the beta derivative. This aforesaid equation is a significant model which deals with a variety of nonlinear phenomenons in the fields of optical fibers, physics and other scientific fields. A complex wave hypothesis is applied to transform the aforesaid system with beta derivative into an ODE system. The modified Kudryashov’s (New version) and the Auxiliary equation methods are used to build a variety of soliton solutions of HMs with beta derivative. The singular, periodic, bright, dark solitons and mixed solitons are constructed with the above-mentioned approaches via soft computation in Mathematica. Some of them are numerically simulated for 2D and 3D representation. All the solutions of fractional HMs produced by the above two techniques are novel and have not been derived yet.

Novel precise solutions and bifurcation of traveling wave solutions for the nonlinear fractional (3 + 1)-dimensional WBBM equation

The nonlinear fractional differential equations (FDEs) are composed by mathematical modeling through nonlinear corporeal structures. The study of these kinds of models has an energetic position in different fields of applied sciences. In this study, we observe the dynamical behavior of nonlinear traveling waves for the [Formula: see text]-fractional [Formula: see text]-dimensional Wazwaz–Benjamin–Bona–Mohany (WBBM) equation. Novel exact traveling wave solutions in the form of trigonometric, hyperbolic and rational functions are derived using [Formula: see text], modified [Formula: see text] and new extended direct algebraic methods with the help of symbolic soft computation. We guarantee that all the obtained results are new and verified the main equation. To promote the essential propagated features, some investigated solutions are exhibited in the form of 2D and 3D graphics by passing on the precise values to the parameters under the constrain conditions, and this provides useful information about the dynamical behavior. Further, bifurcation behavior of nonlinear traveling waves of the proposed equation is studied with the help of bifurcation theory of planar dynamical systems. It is also observed that the proposed equation support the nonlinear solitary wave, periodic wave, kink and antikink waves and most important supernonlinear periodic wave.

Soliton Solutions and Sensitive Analysis of Modified Equal-Width Equation Using Fractional Operators

In this manuscript, the novel auxiliary equation methodology (NAEM) is employed to scrutinize various forms of solitary wave solutions for the modified equal-width wave (MEW) equation. M-truncated along with Atangana–Baleanu (AB)-fractional derivatives are employed to study the soliton solutions of the problem. The fractional MEW equations are important for describing hydro-magnetic waves in cold plasma. A comparative analysis is utilized to study the influence of the fractional parameter on the generated solutions. Secured solutions include bright, dark, singular, periodic and many other types of soliton solutions. In compared to other methods, the solutions demonstrate that the proposed technique is particularly effective, straightforward, and trustworthy that contains families of solutions. In addition, the symbolic soft computation is used to verify the obtained solutions. Finally, the system is subjected to a sensitive analysis. Integer-order results calculated by the symmetry method present in the literature can be addressed as limiting cases of the present study.

Design and fabrication of jean substrate based textile antenna for GSM band application

Soft computation is well equipped and successfully implemented in the field of antenna design in this paper ANN and Genetic algorithms are used to design a fabric based micro strip patch antenna at the center frequency, fc of 1.45 GHz. Textile materials are apt to be produced in the form of wearable antenna substrates owing to their reduced dielectric constant or relative permittivity properties. Specifically, in this article, jeans fabric having the relative permittivity, ℇr = 1.70, along with a thickness of 1.00 mm is selected to be a substrate. Initially, Radial basis networks is used to design a micro strip patch antenna configuration equipped with an edge feeding mechanism. Then , Genetic algorithm is applied for optimization of proposed antenna parameters Finally Wearable textile micros trip patch antenna is created, merged, and concealed within the clothing, correctly. It is revealed from simulation and fabrication measurement outcomes that the proposed antenna properties are desirable for a body centric communication, which is at the fc = 1.45 GHz.

Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems

In multiuser massive Multiple Input Multiple Output (MIMO) systems, a large amount of antennas are deployed at the Base Station (BS). In this case, the Minimum Mean Square Error (MMSE) detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation. The optimization algorithms such as Gradient Descent (GD) method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation. However, they converge slowly when the condition number of the MMSE filtering matrix (the coefficient matrix) increases, which can compromise the efficiency of their implementation. Moreover, their soft information computation also involves a large-scale matrix-matrix multiplication operation. In this paper, a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent (APGD-SOD) method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems. In the proposed detector, an Adaptive Pre-conditioner (AP) matrix obtained through the Quasi-Newton Symmetric Rank One (QN-SR1) update in each iteration is used to accelerate the convergence of the GD method. The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix, since then the condition number would be close to unity and the convergence would be rapid. By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems, the computational complexity of the soft information is simplified. The proposed AP matrix is applied to the GD method as a showcase. However, it also can be used by Conjugate Gradient (CG) method due to its generality. It is demonstrated that the proposed detector is robust and its convergence rate is superlinear. Simulation results show that the proposed detector converges at most four iterations. Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation, which entails a big challenge for the efficient implementation.

Fuzzy Logic, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference Methodology for Soft Computation and Modeling of Ion Sensing Data of a Terpyridyl-Imidazole Based Bifunctional Receptor.

Anion and cation sensing aspects of a terpyridyl-imidazole based receptor have been utilized in this work for the fabrication of multiply configurable Boolean and fuzzy logic systems. The terpyridine moiety of the receptor is used for cation sensing through coordination, whereas the imidazole motif is utilized for anion sensing via hydrogen bonding interaction and/or anion-induced deprotonation, and the recognition event was monitored through absorption and emission spectroscopy. The receptor functions as a selective sensor for F- and Fe2+ among the studied anions and cations, respectively. Interestingly, the complexation of the receptor by Fe2+ and its decomplexation by F- and deprotonation of the receptor by F- and restoration to its initial form by acid are reversible and can be recycled. The receptor can mimic various logic operations such as combinatorial logic gate and keypad lock using its spectral responses through the sequential use of ionic inputs. Conducting very detailed sensing studies by varying the concentration of the analytes within a wide domain is often very time-consuming, laborious, and expensive. To decrease the time and expenses of the investigations, soft computing approaches such as artificial neural networks (ANNs), fuzzy logic, or adaptive neuro-fuzzy inference system (ANFIS) can be recommended to predict the experimental spectral data. Soft computing approaches to artificial intelligence (AI) include neural networks, fuzzy systems, evolutionary computation, and other tools based on statistical and mathematical optimizations. This study compares fuzzy, ANN, and ANFIS outputs to model the protonation-deprotonation and complexation-decomplexation behaviors of the receptor. Triangular membership functions (trimf) are used to model the ANFIS methodology. A good correlation is observed between experimental and model output data. The testing root mean square error (RMSE) for the ANFIS model is 0.0023 for protonation-deprotonation and 0.0036 for complexation-decomplexation data.

AN APPLICATION OF SOFT COMPUTING TECHNIQUES TO PREDICT DYNAMIC BEHAVIOUR OF MOORING SYSTEMS

A spread mooring system (SMS) allows a ship or a floating platform to moor the seafloor using multiple mooring lines at a restricted region with a fixed heading in harsh weather. These systems can be used for the operations of ships of different tonnage at different sea depths. The optimal design of these systems is a challenging engineering problem because of the effects of many design parameters and changing environmental conditions. Modern soft computing techniques allow difficult engineering problems to be solved easily and precisely and are becoming more and more popular. In this paper, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) as soft computation techniques have been chosen to estimate the hawser tensions and displacements of a spread mooring system. The attained results show both techniques can give consistent indicators for the modelling of dynamic systems. Although these techniques performed very well, the ANFIS model is relatively superior to the ANN technique, considering the accuracy of hawser tensions and displacements in terms of the relative errors and coefficient of correlation obtained for the ANN and ANFIS.

Discharge modeling in compound channels with non-prismatic floodplains using GMDH and MARS models

Abstract In this study, modeling of discharge was performed in compound open channels with non-prismatic floodplains (CCNPF) using soft computation models including multivariate adaptive regression splines (MARS) and group method of data handling (GMDH), and then their results were compared with the multilayer perceptron neural networks (MLPNN). In addition to the total discharge, the discharge separation between the floodplain and main channel was modeled and predicted. The parameters of relative roughness coefficient, the relative area of flow cross-section, relative hydraulic radius, bed slope, the relative width of water surface, relative depth, convergence or divergence angle, relative longitudinal distance as inputs, and discharge were considered as models output. The results demonstrated that the statistical indices of MARS, GMDH, and MLPNN models in the testing stage are R2 = 0.962(RMSE = 0.003), 0.930(RMSE = 0.004), and 0.933(RMSE = 0.004) respectively. Examination of statistical error indices shows that all the developed models have the appropriate accuracy to estimate the flow discharge in CCNPF. Examination of the structure of developed GMDH and MARS models demonstrated that the relative parameters: roughness, area, hydraulic radius, flow aspect ratio, depth, and angle of convergence or divergence of floodplain have the greatest impact on modeling and estimation of discharge.

Dynamics of new optical solitons for the Triki–Biswas model using beta-time derivative

This paper comprises the different types of optical soliton solutions of an important Triki–Biswas model equation with beta-time derivative. The beta derivative is considered as a generalized version of the classical derivative. The aforesaid model equation is the generalization of the derivative nonlinear Schrödinger equation that describes the ultrashort pulse propagation with non-Kerr dispersion. The study is carried out by means of a novel beta derivative operator and three efficient integration schemes. During this work, a sequence of new optical solitons is produced that may have an importance in optical fiber systems. These solutions are verified and numerically simulated through soft computation.