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This article examines the problem of estimating the states of Markovian jumping competitive neural networks, where the estimation is done using stochastic sampled-data control with time-varying delay. Instead of continuously measuring the states, the network relies on sampled measurements, and a sampled-data estimator is proposed. The estimator uses probabilistic sampling during two sampling periods, following a Bernoulli distribution. The article also takes into account the possibility of actuator failure in real systems. To ensure the exponentially mean-square stability of the delayed neural networks, the article constructs a Lyapunov-Krasovskii functional (LKF) that includes information about the bounds of the delay. The sufficient conditions for stability are derived in the form of linear matrix inequalities (LMIs) by employing modified free matrix-based integral inequalities. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.

This paper presents a real-time kinematically synchronous planning method for the collaborative manipulation of a multi-arms robot with physical coupling based on the self-organizing competitive neural network. This method defines the sub-bases for the configuration of multi-arms to obtain the Jacobian matrix of common degrees of freedom so that the sub-base motion converges along the direction for the total pose error of the end-effectors (EEs). Such a consideration ensures the uniformity of the EE motion before the error converges completely and contributes to the collaborative manipulation of multi-arms. An unsupervised competitive neural network model is raised to adaptively increase the convergence ratio of multi-arms via the online learning of the rules of the inner star. Then, combining with the defined sub-bases, the synchronous planning method is established to achieve the synchronous movement of multi-arms robot rapidly for collaborative manipulation. Theory analysis proves the stability of the multi-arms system via the Lyapunov theory. Various simulations and experiments demonstrate that the proposed kinematically synchronous planning method is feasible and applicable to different symmetric and asymmetric cooperative manipulation tasks for a multi-arms system.

The Winner-Take-All circuit is an important part of the competition layer in the competitive neural network. Its main function is to compare the size of the output of the nodes after the weighted summation of all input vectors, and select the node with the largest output to output high power level, while other nodes output low level, that is, to find the node with the largest output. According to the characteristics of the Winner-Take-All circuit in the competitive neural network, the simulation of the Winner-Take-All circuit is carried out by the PSPICE simulation software. The physical test results show that, like the simulation diagram of the Winner-Take-All circuit, it conforms to the logic truth table, which further confirms the rationality and correctness of the Winner-Take-All circuit. Hardware realization of Winner-Take-All circuit as an important component of competitive layer in competitive neural networks has important research significance.

In this paper, a Competitive Neural Network circuit based on voltage-controlled memristors is proposed, of which the synapse structure is one memristor (1M). The designed circuit consists of the forward calculation part and the weight updating part. The forward calculation part is designed according to the winner-take-all mechanism, in which the m -LIF model and PMOS transistors with switching characteristics are combined to achieve the lateral inhibition. The weight updating part is designed based on the Hebbian learning rule. By using the voltage controlled switches, only the synaptic memristors connected to the winner output neuron obtained from the forward calculation part are adjusted. The whole circuit does not require the participation of CPU, FPGA or other microcontrollers, providing the possibility to realize computing-in-memory and parallel computing. We perform simulation experiments of unsupervised online learning of 5*3 pixels patterns and 28*28 pixels patterns based on the designed circuit in PSPICE. The changing trend of the network weights during the training phase and the high recognition accuracy in the recognition phase  verify the network can effectively learn and recognize different patterns.

In this paper, we investigate a class of fuzzy memristive competitive neural networks with different time scales. Based on Lyapunov functional and differential inclusions, two proper controllers are designed to achieve the anti-synchronization of systems. Some novel results have been obtained for anti-synchronization. Finally, an example is given to illustrate the effectiveness of our main results.

Since the fixed-time stability forms of nonlinear systems satisfy strict conditions, there are few general forms for nonlinear systems to achieve fixed-time stability. This work proposes a new class of more general fixed-time stability criteria. It is worth mentioning that, compared with the traditional method of estimating the convergence time, this paper obtains a more conservative stable time estimation formula through the integration method of the generalized integral mean theorem. In addition, given that the fixed-time stabilization of neural networks and chaotic oscillators have attracted extensive attention in recent years, and there are still many fixed-time stabilizations of nonlinear systems that have not been studied. Therefore, a discontinuous controller is designed in this paper. The above stability theory results are applied to the fixed-time stabilization of the Takagi-Sugeno (T-S) fuzzy competitive neural network and chaotic system (coupled Chua’s oscillator). Finally, the validity and applicability of the theoretical results are verified by examples.

Without altering the inertial system into the two first-order differential systems, this paper primarily works over the global exponential dissipativity (GED) of memristive inertial competitive neural networks (MICNNs) with mixed delays. For this purpose, a novel differential inequality is primarily established around the discussed system. Then, by applying the founded inequality and constructing some novel Lyapunov functionals, the GED criteria in the algebraic form and the linear matrix inequality (LMI) form are given, respectively. Furthermore, the estimation of the global exponential attractive set (GEAS) is furnished. Finally, a specific illustrative example is analyzed to check the correctness and feasibility of the obtained findings.

A competitive neural network model was proposed to describe the dynamics of cortical maps in which, there exist two memories: long-term and short-term. In this paper, we investigate the existence and the exponential stability of the pseudo-almost periodic solution of a system of equations modeling the dynamics of neutral-type competitive neural networks with mixed delays in the time-space scales for the first time. The mixed delays include time-varying delays and continuously distributed ones. Based on contraction principle and the theory of calculus on time-space scales, some new criteria proving the convergence of all solutions of the networks toward the unique pseudo-almost periodic solution are derived by using the ad-hoc Lyapunov–Krasovskii functional. Finally, numerical example with graphical illustration is given to confirm our main results.

In this paper, the exponential stability for a class of delayed competitive neural networks is studied. By applying the inequality technique and non-reduced-order approach, some novel and useful criteria of global exponential stability for the addressed network model are established. Moreover, a numerical example is presented to show the feasibility and effectiveness of the theoretical results.

This paper investigates the cluster synchronization problem of coupled delayed competitive neural networks (CNNs) with two time scales. Each CNN contains short- and long-term memories, which can be regarded as the fast and slow dynamics, respectively. Besides, a general communication topology that describes both cooperation and competition in CNN-to-CNN relations is considered along with fixed and adaptive coupling schemes. The interactive relationship between the fast and slow dynamics as well as the effects of the fast time scale on synchronization behavior has not been fully exploited in existing Lyapunov functionals. Moreover, the results from pervious works are limited to the master–slave synchronization of two CNNs. In this paper, a novel Lyapunov–Krasovskii functional is proposed to solve the cluster synchronization problem under the fixed coupling scheme. The coupled delayed CNNs within a specific range of the fast time scale achieve a desirable behavior when the coupling and pinning strengths are chosen properly. Furthermore, to facilitate the selection of these strengths, an adaptive pinning controller is designed and a modified Lyapunov–Krasovskii functional is also constructed for coupled delayed CNNs with two time scales. Finally, several numerical examples are provided to demonstrate the effectiveness of the theoretical results.