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This article deals with the development of four modified radial basis function neural network (RBFNN) models. The corresponding learning algorithms associated with the updating of internal parameters of the models are derived. The conventional inputs are used in the first and second modified RBFNN models (models 3 and 4) whereas exponential nonlinear inputs are used in the fifth and sixth RBFNN models to provide additional nonlinearity for achieving a better solution of nonlinear classification, and direct and inverse modeling problems. To assess and compare the performance potentiality of the proposed four new RBFNN models, one classification problem, one direct modeling problem, and one inverse modeling problem are solved through computer simulation-based experiments. For comparison and to assign the performance rank of each of the four modified RBFNN models, two conventional and commonly used RBFNN models (models 1 and 2) are also simulated. To access the performance of different models during the training phase of Examples 1 and 2, the root mean-square error (RMSE) value, mean absolute deviation (MAD), and the number of iterations required to achieve convergence are obtained. For the third example, only the first two performance measures are found. During the testing or validation phase, the output responses of the different models of Example 2 are compared with the desired response analysis. For Example 3, the bit-error rate (BER) plots are compared. The observation of all the results demonstrates consistent ranks of all models in the case of all three examples. It is, in general, found that the ranks of the models 1–6 are 6, 4, 3, 2, 5, and 1, respectively. In essence, in terms of all performance measures, model M-6 with an exponential version of inputs with weights on both layers occupies the first position whereas model M-4 with conventional inputs as the second position. [ABSTRACT FROM AUTHOR]

The upper-limb prosthesis has been extensively studied using electromyography (EMG) signals to overcome the physical and functional deficiencies of amputees in recent years. However, most studies focus on the discrete classification of gestures and ignore the interconnection between the classification results and the neuroprosthesis control interface, which plays a vital role in system development. In this article, a new continuous control scheme is proposed to achieve an effective control of the developed upper-limb prosthesis. It utilizes eight channels of EMG signals of the human upper limb to model and control the developed prosthesis. A continuous control scheme is proposed that combines the state of the system and the decoding results to dynamically produce the expected angular velocity of the joint based on the results of the classification. Finally, experiments are performed to demonstrate the effectiveness of the proposed algorithm using an upper-limb neuroprosthesis, achieving the reach and grasp tasks. The results showed that it improves performance with a regular angular velocity of the joint, which underlines the importance of an adequate control scheme for the EMG-guided prosthesis. [ABSTRACT FROM AUTHOR]

The purpose of this article is to present a novel biobjective biform game framework for optimizing strategy selection of nodes (agents) in bilateral link network formation under dual objectives of profit and link security. First, in the first part of the noncooperative game, we construct various possible strategy combinations (situations) of all players (nodes) in the network, which define the competitive environment in the second part of the cooperative game. Second, in the second part, we derive coalitions’ payoffs under the two objectives of profit and link security and propose the biobjective Banzhaf values to allocate the corresponding cooperative games in all strategy situations. Then, a biobjective noncooperative game is formed on the basis of the obtained biobjective Banzhaf values. Third, we redefine Pareto–Nash equilibriums (in the sense of pure strategies) and obtain the Pareto–Nash equilibriums of the biobjective noncooperative game in the first part. Subsequently, the choice of the obtained Pareto–Nash equilibriums is regarded as a biobjective group decision-making problem, which is solved by employing the redefined Borda choice function method with objective weights. Hence, we can obtain the optimal strategy choices of all the nodes (players) for the entire bilateral link network formation. Finally, numerical examples and comparative analysis are given to verify the validity and applicability of the proposed models and methods. [ABSTRACT FROM AUTHOR]

Recent studies have examined minimum cost consensus models from the moderator’s perspective. However, current dual models aimed at deriving a maximum return have been limited to special cases. A further problem has been the return allocation to the individuals. With this in mind, this article presents two general dual consensus models, the first of which is a minimum cost consensus model, and the second of which allows the different individuals to have different tolerance levels. In both models the individual opinions and the group opinion are connected using a weighted average operator. To understand the economic significance of these models, some properties, such as the weak duality property, the optimality property, and the strong duality property are given. The relationships between the individual opinions, the unit costs, and the unit returns are also proven, and the core return allocation problem is addressed. It was found that in both cases, the costs the moderator needed to pay to a given individual were precisely equal to the returns the individual deserved. Numerical case studies were used to illustrate the proposed models, with the simulation results providing insights into the practical use of these optimization consensus models. [ABSTRACT FROM AUTHOR]

This article intends to provide an integrated robust synchronization and anti-disturbance control design for a nonlinear multiweighted complex network with the use of fuzzy modeling approach. The network takes the effect of both matched and mismatched disturbances into account, wherein the matched one is unknown and caused by some exogenous systems. In order to estimate the unknown disturbance, a disturbance observer is precisely considered. By exploiting the parallel distributed compensation strategy and the output of disturbance observer, the desired fuzzy rule-based control law is formulated. Based on these settings, the required control gain matrices that affirm asymptotic synchronization of the addressed network are determined with the support of the Lyapunov functional method and extended Wirtinger’s integral inequality. The method of this article can render better system performance than the existing $H_{\infty }$ control method, which is demonstrated via simulations. In addition, a three-dimensional Lorenz chaotic model is employed to certify the applicability of the theoretical findings. [ABSTRACT FROM AUTHOR]

This article concentrates on the global uniform exponential stability analysis and dissipativity property for a class of discrete-time gene regulatory networks (GRNs). To describe the processes at work as cells change phenotype, the time-dependent persistent dwell-time switching strategy is applied to switched GRNs. Formulating the mode-dependent Lyapunov–Krasovskii functional, the global uniform exponential stability and strictly dissipativity property criteria for the GRNs subject to time-varying delays are presented. Ultimately, an example, including three cases is employed to illustrate and discuss the availability of the given criteria. [ABSTRACT FROM AUTHOR]

This article deals with the problem of designing nonlinear distributed control laws for continuous-time networked nonlinear heterogeneous systems with bounded sector nonlinear interconnections. The networked system is a combination of interconnected Takagi–Sugeno (TS) fuzzy systems with nonlinear consequent subject to state and control input constraints. The new sufficient conditions, taking into account state constraints and actuators saturation, are derived in terms of linear matrix inequalities. Further, it is shown that the closed-loop system can be made asymptotically stable while reducing the conservatism over decentralized and linear distributed control laws derived following the same method. At the same time, the maximization of the estimate of the domain of attraction (DoA) for the closed-loop system is pursued. Finally, two cases are presented to illustrate the effectiveness of the proposed design approach: 1) an electrical power system composed of two-machine subsystems and 2) a network of multiple inverted pendulums connected by nonlinear springs. [ABSTRACT FROM AUTHOR]