Takagi-Sugeno Fuzzy Neural Networks Research Articles

Quasi-Static Modelling of a Full-Channel Effective Magnetorheological Damper with Trapezoidal Magnetic Rings.

Magnetorheological damper (MRD) has been successfully applied to vehicle suspension systems as an intelligent core component. Most conventional MRDs have closed rectangle-shaped magnetic circuits, resulting in a short effective working length and negligible damping force. To address the above issues, a novel full-channel effective MRD with trapezoidal magnetic rings (FEMRD_TMR) is proposed. The trapezoidal magnetic ring can shunt the magnetic circuit, distributing it evenly along the damping channel and increasing the effective working length. Additionally, which has the same variation trend as the magnetic flux through it, makes the magnetic induction intensity distribution more uniform to reduce the magnetic saturation problem. Theoretically analyzing the damping characteristics of the FEMRD_TMR, a quasi-static model is developed to forecast the output damping force. The structural design of MRD is challenging since conventional quasi-static models rely on the yield stress of magnetorheological fluid (MRF) to reflect the rheological property, which cannot be directly observed and is challenging to calculate. The Takagi-Sugeno (T-S) fuzzy neural network and a unique magnetic circuit computation are offered as a novel quasi-static modeling approach to address the issue. The MRF's yield stress is linearized into magnetic induction intensity functions by the T-S fuzzy neural network and then converted into the MRD's structural size by the special magnetic circuit calculation. Therefore, the proposed quasi-static model can directly reflect the relationship between the damping force and structure size, simplifying MRD's structure design. The novel quasi-static model is shown to be more straightforward and understandable than the conventional Bingham quasi-static model and to have approximately accurate damping force prediction when compared to experimental data.

Event-Triggered Synchronization for Delayed Quaternion-Valued Inertial Fuzzy Neural Networks via Nonreduced Order Approach

This paper mainly studies the synchronization problem for a class of Takagi-Sugeno (T-S) fuzzy quaternion-valued inertial neural networks with time-varying delay through event-triggered control (ETC) scheme. Firstly, a class of quaternion-valued inertial fuzzy neural networks (QVIFNNs) model with time-varying delay is proposed. To avoid the increase of the number of state variables caused by reduced-order method, non-reduced order approach is utilized fully, and a fuzzy exponential gain event-triggered controller is designed so as to occupy the less communication resources. Then, by establishing a novel Lyapunov functional well handling the high-order term, a new synchronization condition is derived under static event-triggered scheme. And then, to further reduce the number of triggers, a dynamic event-triggered condition is developed and the corresponding sufficient criterion is given. Meanwhile, it is proved that the lower bound of event intervals are non-zero positive and the Zeno phenomenons do not exist via rigorous mathematical derivation. Finally, a numerical example is offered to show the effectiveness of the proposed method. A application example for the considered QVIFNNs with time-varying delay is given.

Enhanced Evaluation Model Based on Classification Selection Applied to Value Evaluation of Waste Household Appliances

In the process of recycling, dismantling, and reusing household appliances, implementing extended producer responsibility (EPR) has become increasingly important. Designing a reasonable pricing mechanism for waste household appliance recycling is critical for the implementation of EPR. To address the problem of labor-intensive and experience-dependent traditional manual methods for assessing the value of waste household appliances, in this paper, we propose an evaluation method based on the subtractive clustering method and an adaptive neuro fuzzy inference system (SCM–ANFIS), which outperforms traditional neural networks such as LSTM, BP neural network, random forest and Takagi–Sugeno fuzzy neural network (T–S FNN). Moreover, in this paper, we combine the five aforementioned algorithms to design a combination evaluation model based on maximum ratio combination (CEM–MRC), which can achieve a performance improvement of 0.1% in terms of mean absolute percentage error (MAPE) compared to the suboptimal BP neural network. Furthermore, an enhanced evaluation model based on classification selection (EEM–CS) is designed to automatically select the evaluation results between the optimal SCM–ANFIS and the suboptimal CEM–MRC, resulting in a 0.73% reduction in MAPE compared to the optimal SCM–ANFIS and a 1.42% reduction compared to the suboptimal CEM–MRC. In this paper, we also validate the performance of the proposed algorithms using a dataset of waste television recycling, which demonstrates the high accuracy of the proposed value assessment mechanisms achieved without human intervention and a significant improvement in evaluation accuracy as compared to conventional neural-network-based algorithms.

System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field

<abstract><p>Stochastic disturbances often occur in real-world systems which can lead to undesirable system dynamics. Therefore, it is necessary to investigate stochastic disturbances in neural network modeling. As such, this paper examines the stability problem for Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks. By applying Takagi-Sugeno fuzzy models and stochastic analysis, we first consider a general form of Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks with time-varying delays. Then, by constructing suitable Lyapunov-Krasovskii functional, we present new delay-dependent robust and global asymptotic stability criteria for the considered networks. Furthermore, we present our results in terms of real-valued linear matrix inequalities that can be solved in MATLAB LMI toolbox. Finally, two numerical examples are presented with their simulations to demonstrate the validity of the theoretical analysis.</p></abstract>

Game Theory for Distributed IoV Task Offloading With Fuzzy Neural Network in Edge Computing

The development of the Internet of vehicles (IoV) has spawned a series of driving assistance services (e.g., collision warning), which improves the safety and intelligence of transportation. In IoV, the driving assistance services need to be met in time due to the rapid speed of vehicles. By introducing edge computing into the IoV, the insufficiency of local computation resources in vehicles is improved, providing high quality services for users. Nevertheless, the resources provided by edge servers are often limited, which fail to meet all the needs of users in IoV simultaneously. Thereby, how to minimize the tasks processing latency of users in the case of limited edge server resources is still a challenge. To handle the above problem, a task offloading scheme fuzzy-task-offloading-and-resource-allocation (F-TORA) based on Takagi–Sugeno fuzzy neural network (T–S FNN) and game theory is designed. Primarily, the cloud server predicts the future traffic flow of each section through T–S FNN and transmits the prediction results to the roadside units (RSUs). Then, the RSU adjusts the current load based on the captured future traffic flow data. After the load balancing of each RSU, the optimal task offloading strategy is determined for the users by game theory. Following, the edge server acts as an agent to allocate computing resources for the offloaded tasks by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning algorithm. Finally, the robust performance of the proposed method is validated by comparative experiments.

Global stability of Clifford-valued Takagi-Sugeno fuzzy neural networks with time-varying delays and impulses

Global stability of Clifford-valued Takagi-Sugeno fuzzy neural networks with time-varying delays and impulses

Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network

Adjusting tillage parameters according to soil conditions can reduce energy consumption. In this study, the working parameters and soil physical parameters of plowing were determined using a designed electric suspension platform and soil instrument. The soil conditions were classified into three physical states, namely ‘hard’, ‘zero’, and ‘soft’ using a fuzzy C-means clustering algorithm, taking the soil moisture content and cone penetration resistance as the grading indexes. The Takagi–Sugeno (T–S) fuzzy neural network classifier was constructed using traction resistance, operating velocity, and plowing depth as inputs to indirectly identify the soil’s physical state. The results show that when 280 groups of test data were used to verify the model, 264 groups were correctly identified, indicating a soil physical state identification accuracy of 94.29%. The T–S fuzzy neural network prediction model can achieve the real-time and accurate physical state identification of paddy soil during plowing.

Takagi–Sugeno fuzzy based power system fault section diagnosis models via genetic learning adaptive GSK algorithm

To effectively deal with the operating uncertainties of protective relays and circuit breakers existing in the power system faults, an improved fault section diagnosis (FSD) method is proposed by using Takagi–Sugeno fuzzy neural networks (T–S FNN). In this method an optimal T–S FNN-based diagnosis model is built with the idea of distributed parallel processing for each section instead of the whole power system. To obtain accurate T–S FNN-based diagnosis models, a genetic learning adaptive gaining-sharing knowledge-based algorithm (GLAGSK) is designed to optimize their structure parameters and consequent parameters. GLAGSK combines an adaptive knowledge ratio and a genetic learning strategy to balance population diversity and convergence speed to boost the optimization ability. After a fault occurs, selective optimal T–S FNN-based diagnosis models are triggered according to the alarm information. They work in parallel to improve the fault diagnosis efficiency. Simulation results of three test systems including an actual fault event show that, compared with other peer algorithms, GLAGSK can obtain more accurate T–S FNN-based diagnosis models with faster global convergence. Besides, compared with the BP and RBF neural networks and other FSD methods, the proposed FSD method based on optimal T–S FNN-based diagnosis models can diagnose different complex faults successfully with higher fault credibility.

MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration

The Municipal solid wastes incineration (MSWI) process with complex mechanism and strong nonlinearity is an important part of resource cycle. It is a challenge to design its multivariable controlled object model and control strategy. To solve this problem, a multi-input multi-output (MIMO) data-driven model and a multi-loop PID controller are proposed in this paper. Firstly, a feature selection method based on Pearson correlation coefficient (PCC) and expert knowledge is used to analyze the relationship between the manipulated variable and the controlled variable of MSWI process. Secondly, a MIMO Takagi–Sugeno fuzzy neural network (TSFNN) based on multi-task learning (MTL) is designed to construct the multivariable controlled object model. Thirdly, a multi-loop PID controller based on quasi-diagonal recurrent neural network (QDRNN) is constructed, which has self-feedback channel and interconnection channel, and can adjust the control parameters automatically. Next, the stability of control strategy is proved by Lyapunov second method. Finally, the modeling effect and control performance are confirmed on the simulation experiments based on the real MSWI process data.

A modified interval type-2 Takagi-Sugeno fuzzy neural network and its convergence analysis

In this paper, to compute the firing strength values of type-2 fuzzy models, a soft version of minimum is presented, which endows the fuzzy model with the ability to solve large dimensional problems. In addition, a conjugate gradient method is borrowed to train the designed interval type-2 Takagi-Sugeno fuzzy model. Compared with the existing gradient-based learning strategy, this scheme can efficiently enhance the fuzzy model performance. Last but not least, convergence analysis for this modified interval type-2 Takagi-Sugeno fuzzy neural network (MIT2TSFNN) is conducted in detail, which proves that the gradient of the error function tends to zero with the iteration increasing (weak convergence) and the sequence of model parameters (weights) convergences to a fixed point (strong convergence). To validate the effectiveness of the proposed MIT2TSFNN and its theoretical results, simulation results of six regression and six classification problems are presented.

Prediction of dissolved oxygen content changes based on two-dimensional behavior features of fish school and T–S fuzzy neural network

Dissolved oxygen (DO) content is an important index of river water quality. Water quality sensors have been used in China for urban river water monitoring and DO content prediction. However, water quality sensors are expensive and difficult to maintain, and have a short operation period and difficult to maintain. This study developed a scientific and accurate method for prediction of DO content changes using fish school features. The behavioral features of the Carassius auratus fish school were described using two-dimensional fish school images. The degree of DO content decline was graded into five levels, and the corresponding numerical ranges of cluster characteristic parameters were determined by considering the opinions of ichthyologists. Finally, the variation of DO content was predicted using the characteristic parameters of the fish school and the multiple-input single-output Takagi–Sugeno fuzzy neural network. The prediction results were basically consistent with the actual variations of DO content. Therefore, it is feasible to use the behavioral features of the fish school to dynamically predict the level of DO content in water, and this method is especially suitable for prediction of sharp decline of DO content in a relatively short time.

Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River

Assessment of river water quality is very important for understanding the impact of human activities on aquatic ecosystems. As the second-largest river in China, the Yellow River's water environment is closely related to the social development and water security of northern China. The Huangshui River is a major tributary of the upper Yellow River, and it supplies water to cities in the lower reaches. In this study, a Takagi-Sugeno (T-S) fuzzy neural network was used to evaluate water quality of the Huangshui River, and pollutant sources were analyzed. The heavy metal pollution index (HPI) was calculated to assess the heavy metal pollution level, and the health risks posed by heavy metal elements were assessed. The results indicated that the main contaminants in the Huangshui River were ammonia nitrogen (NH3–N) and total phosphorus (TP), which was affected by various activities of industry, agriculture, and urbanization, and the maximum concentration of NH3–N and TP was 5.90 mg/L and 0.36 mg/L, respectively. The T-S evaluation results of some points in the middle reaches were 3.317 and 3.197, which belonged to Level Ⅳ and the water quality was poor. The concentrations of Cu, Zn and Cr in the river were 0.57–44.58 μg/L, 10–122.50 μg/L and 2–28.67 μg/L, respectively, and they were relatively large. The T-S fuzzy neural network could evaluate water quality, avoiding extreme evaluation results by using fuzzy rules to reduce the influence of pollutant concentrations that are too high or too low. In addition to qualitative categorization of water quality, this approach can also quantitatively assess water quality within a single category. The results of water quality assessment could provide a scientific data support for river management.

Hysteresis Modeling and Analysis of Magnetic Shape Memory Alloy-Driven Actuator

The accuracy of the micro-positioning system impelled by a magnetic shape memory alloy-driven actuator (MSMADA), is severely restricted by the frequency-dependent hysteresis nonlinearity. Moreover, the actuating accuracy is further affected by various operating factors such as load and temperature. In this study, a Duhem model (DM) identified online by a Takagi-Sugeno fuzzy neural network (TSFNN-DM) is innovatively proposed for describing the frequency-dependent hysteresis nonlinearity of the MSMADA. The DM, which has the explicit function expression, is one of the popular differential equation-based hysteresis models. However, the determination of the DM parameters is difficult and hinders its further applications. The TSFNN, which combines the advantages of easy expressing of the fuzzy inference system and self-adjustment ability of the NN, is employed to identify the DM parameters online. The rationality of the developed method is proved by a Taylor expansion in theory. Plenty of experiments verify that the proposed TSFNN-DM method is an efficient manner to capture the frequency-dependent hysteresis nonlinearity under different working conditions.

Sensitivity analysis of Takagi–Sugeno fuzzy neural network

In this paper, we first define a measure of statistical sensitivity of a zero-order Takagi–Sugeno (TS) fuzzy neural network (FNN) with respect to perturbation of weights and parameters of the system. Then we derive measures of sensitivity of the system with respect to additive and multiplicative noises to the consequent parameters. For this we consider a multiple-input multiple-output (MIMO) FNN. The derivation can be easily extended to sensitivity with respect to other parameters as well. These measures of sensitivity are then used as regularizers to the loss function while training the system. Finally, to validate the sensitivity-based learning method, another definition of statistical sensitivity measure, based on absolute output error, is proposed, and its corresponding expression for additive/multiplicative perturbations of the consequent parameters is derived as well. Using simulation results on one classification problem and two regression problems, the effectiveness of the sensitivity measures is demonstrated.

Mechanical performance analysis of a piezoelectric ceramic friction damper and research of its semi-active control strategy

Based on piezoelectric ceramics' piezoelectric effect, a new type of piezoelectric ceramic friction damper (PCFD) is designed and manufactured to provide a reliable energy dissipation device for structural response control. In the PCFD, high-strength bolts limit the deformation of piezoelectric ceramics under voltage, and the resulting reaction force changes the preload of high-strength bolts, thereby realizing real-time adjustment of friction by adjusting the positive pressure on the friction surface. The influence of friction plates with different materials on the hysteretic performance of friction damper under different tightening torque is investigated by cyclic loading test to determine the friction plate's selection in the PCFD. The hysteretic performance test is carried out to evaluate the energy dissipation capacity of PCFD with different voltages, and the experimental results are compared with the theoretical values. The finite element model of PCFD is established to analyze the stress distribution of each component of the PCFD under different voltages. Based on the semi-active control characteristics of PCFD, the Takagi-Sugeno fuzzy neural network semi-active control system for frame structure with PCFD is designed. The adaptive learning function of the adaptive network-based fuzzy inference system (ANFIS) is used to generate fuzzy rules and fuzzy neural network controller (FNNC). Finally, the semi-active control simulation of three-storey frame structure model under seismic excitation is carried out by Simulink. The experimental and numerical results show that the PCFD designed in this paper has good energy dissipation capacity, and the maximum control force increases linearly with the increase of voltage. The combination of FNNC, which is established by ANFIS, and PCFD can effectively reduce the structure response.

Global Exponential Stability of Takagi-Sugeno Fuzzy Cohen-Grossberg Neural Network With Time-Varying Delays

In this letter, global exponential stability of Takagi-Sugeno fuzzy Cohen-Grossberg Neural Network (CGNN) with time-varying delay factor has been investigated based on the criteria of non-singular M-matrix and the Lyapunov stability technique. The stability inequality is derived with the help of Lipschitz condition for the nonlinear activation functions and a sufficient condition is shown to verify the criterion of the exponential stability condition for the CGNN with time-varying delay terms, which is described in the presence of delay terms of T-S Fuzzy model. Thus, the global exponential stability for T-S fuzzy CGNN in the presence of time-varying delay terms is derived in an easy way. This letter contains quite a new result for delayed CGNN for the T-S Fuzzy model. Finally, a numerical example is taken to validate the efficiency and unwavering quality, and to exhibit the superiority of the considered method as compared to the existing method for particular cases.

Multistability for Almost-Periodic Solutions of Takagi–Sugeno Fuzzy Neural Networks With Nonmonotonic Discontinuous Activation Functions and Time-Varying Delays

This article investigates the problem of multistability of almost-periodic solutions of Takagi-Sugeno fuzzy neural networks with nonmonotonic discontinuous activation functions and time-varying delays. Based on the geometrical properties of nonmonotonic activation functions, by using the Ascoli-Arzela theorem and the inequality techniques, it is demonstrated that under some reasonable conditions, the addressed networks have a locally exponentially stable almost-periodic solution in some hyperrectangular regions. We also estimate the attraction basins of the locally stable almost-periodic solutions, which indicates that the attraction basins of the locally exponentially stable almost-periodic solution can be larger than original hyperrectangular regions. These results, which include boundedness, globally attractivity, multiple stability, and attraction basins, generalize and improve the earlier publications, and can be extended to monostability and multistability of Takagi-Sugeno fuzzy neural networks with nonmonotonic discontinuous activation functions. Finally, several numerical examples are given to show the feasibility, the effectiveness, and the merits of the theoretical results.

Research on Marine Water Quality Evaluation Model Based on Improved Harmony Search Algorithm by Gaussian Disturbance to Optimize Takagi-Sugeno Fuzzy Neural Network

Shen, G., 2020. Research on marine water quality evaluation model based on improved harmony search algorithm by Gaussian disturbance to optimize Takagi-Sugeno fuzzy neural network. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 283–287. Coconut Creek (Florida), ISSN 0749-0208.Marine water quality is affected by many factors and is difficult to evaluate accurately. An improved harmony search algorithm based on Gaussian disturbance (GDHS) is proposed to optimize the Takagi-Sugeno (T-S) fuzzy neural network (TSFNN) model of marine water quality evaluation. First, the basic harmony search algorithm is improved by the Gaussian disturbance global optimal harmony strategy and dynamic adjustment parameters. Second, the parameters of the T-S fuzzy neural network are optimized by GDHS to improve the stability and global convergence speed of the TSFNN. Finally, China's marine water quality standards and the actual conditions of the marine area to which the experimental data belong are considered, five indicators are selected to construct a neural network, and the water quality evaluation results of the data of 10 monitoring stations in the eastern waters of Qingdao show that the smaller the evaluation error of the GDHS-TSFNN model, the higher the accuracy.

Layered control of forklift lateral stability based on Takagi–Sugenofuzzy neural network

An anti-roll hydraulic cylinder is designed as an executive mechanism of the lateral stability control to provide lateral support force on the basis of an analysis of the structure of the forklift and the mechanism of lateral instability. A lateral stability layered control method based on Takagi–Sugeno (T–S) fuzzy neural network is proposed, divides the forklift lateral stability control into the upper identification layer, the middle control layer and the lower executive layer. Simulation and real vehicle test results indicate that the forklift lateral stability control method based on the T–S fuzzy neural network can effectively identify the forklift driving state, reduce the forklift rollover possibility and improve forklift safety under limited working conditions.

Intermittent Stabilization of Fuzzy Competitive Neural Networks With Reaction Diffusions

This article investigates the global exponential stability and stabilization problems for a class of Takagi-Sugeno (T-S) fuzzy competitive neural networks (NNs). In the considered model, we introduce the T-S fuzzy rule to describe the parametric switching causing by complexity and the vagueness in practical environment. Besides, the effects of reaction diffusions and distributed delays, which inherently exist in circuits of NNs, are also taken into consideration. By using the Lyapunov functional theory and Green formula, several stability criteria in terms of \mathbb p-norm are established for the uncompensated fuzzy competitive NNs. Moreover, by designing a fuzzy intermittent controller, the corresponding stabilizability criteria in terms of \mathbb p-norm are derived. We also carry out some discussions and comparisons to further show the less conservativeness and wide applicability of the main theorems. Finally, several examples are presented to verify the obtained results.