Application of MBAM Neural Network in CNC Machine Fault Diagnosis

In order to improve the bidirectional associative memory(BAM) performance, a modified BAM model(MBAM) is used to enhance neural network(NN)’s memory capacity and error correction capability, theoretical analysis and experiment results illuminate that MBAM performs much better than the original BAM. The MBAM is used in computer numeric control(CNC) machine fault diagnosis, it not only can complete fault diagnosis correctly but also have fairly high error correction capability for disturbed Input Information sequence.Moreover MBAM model is a more convenient and effective method of solving the problem of CNC electric system fault diagnosis.

Exponential stability and periodic solution for fuzzy BAM Neural networks with time varying delays

In this paper, a class of fuzzy BAM neural networks with time varying delays is discussed. By using the properties of M-matrix, Linear Matrix Inequality（LMI） approach and general Lyapunov-Krasovskii functional, some new sufficient conditions are derived to ensure the existence of periodic solutions and the global exponential stability of the fuzzy BAM neural networks with time varying delays. These results have important significance in the design of global exponential stable BAM networks with delays. Moreover, an example is given to illustrate that the conditions of the results in the paper are feasible.

LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks

Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These conditions were formulated as easily verifiable linear matrix inequalities (LMIs), whose conservativeness is relatively low. The approach proposed extends the known stability results, and can also be applied to other forms of recurrent neural networks (RNNs).

Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach

Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.

Dynamic Analysis of Fractional-Order Fuzzy BAM Neural Networks with Delays in the Leakage Terms

In this paper, based on the theory of fractional-order calculus, we obtain some sufficient conditions for the uniform stability of fractional-order fuzzy BAM neural networks with delays in the leakage terms. Moreover, the existence, uniqueness and stability of its equilibrium point are also proved. A numerical example is presented to demonstrate the validity and feasibility of the proposed results.

Periodic Solutions of Cohen-Grossberg-Type BAM Neural Networks with Time-Varying Delays

Sufficient conditions to guarantee the existence and global exponential stability of periodic solutions of a Cohen-Grossberg-type BAM neural network are established by suitable mathematical transformation.

Existence and Stability Analysis of Fractional Order BAM Neural Networks with a Time Delay

Based on the theory of fractional calculus, the contraction mapping principle, Krasnoselskii fixed point theorem and the inequality technique, a class of Caputo fractional-order BAM neural networks with delays in the leakage terms is investigated in this paper. Some new sufficient conditions are established to guarantee the existence and uniqueness of the nontrivial solution. Moreover, uniform stability of such networks is proposed in fixed time intervals. Finally, an illustrative example is also given to demonstrate the effectiveness of the obtained results.

Delay-Dependent Exponential Stability Criterion for BAM Neural Networks with Time-Varying Delays

By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neural networks with time-varying delays.The proposed condition can be checked easily by LMI control toolbox in Matlab.A numerical example is given to demonstrate the effectiveness of our results.

Global exponential p-norm stability of BAM neural networks with unbounded time-varying delays:A method based on the representation of solutions

This paper studies the global exponential p-norm stability of bidirectional associative memory(BAM)neural networks with unbounded time-varying delays.A novel method based on the representation of solutions is put forward to deduce a global exponential p-norm stability criterion.This method does not need to set up any Lyapunov-Krasovskii functionals(LKF),which can greatly reduce a large amount of computations and is simpler than the existing methods.In the end,representative numerical examples are given to llustrate the availability of the method.

Global Asymptotic Synchronization of a Class of BAM Neural Networks with Time Delays via Integrating Inequality Techniques

In this paper,the authors are concerned with global asymptotic synchronization for a class of BAM neural networks with time delays.Instead of using Lyapunov functional method,LMI method and matrix measure method which are recently widely applied to investigating global exponential/asymptotic synchronization for neural networks,two novel sufficient conditions on global asymptotic synchronization of above BAM neural networks are established by using a kind of new study method of global synchronization:Integrating inequality techniques.The method and results extend the study of global synchronization of neural networks.

Existence and Global Exponential Stability of Pseudo Almost Periodic Solutions of a General Delayed BAM Neural Networks

This paper studies a class of general BAM neural networks with multiple delays. Em- ploying the exponential dichotomy theory and fixed point method, together with constructing suitable Lyapunov functionals, easily verifiable delay-independent criteria are established to ensure the exis- tence and global exponential stability of pseudo almost periodic solutions, which not only generalize but also complement some existing ones. These theoretical results are also supported with numerical simulations.

Exponential stability analysis for neutral BAM neural networks with time-varying delays and stochastic disturbances

This paper is concerned with the global exponential stability analysis problem for a class of neutral bidi- rectional associative memory （BAM） neural networks with time-varying delays and stochastic disturbances. The stochastic disturbances are described by state-dependent stochastic processes. By utilizing an appropriately constructed Lyapunov- Krasovskii functional （LKF） and some stochastic analysis approaches, novel delay-dependent conditions are established in terms of linear matrix inequalities （LMIs）, which can be easily solved by existing convex optimization techniques. Further- more, the exponential convergence rate can be estimated based on the obtained results. An illustrate example is given to demonstrate the effectiveness of the proposed methods.

STABILITY OF DISCRETE-TIME COHEN-GROSSBERG BAM NEURAL NETWORKS WITH DELAYS

In this paper, we study the existence and stability of an equilibrium of discrete-time Cohen-Grossberg BAM Neural Networks with delays. We obtain several sufficient conditions ensuring the existence and stability of an equilibrium of such systems, using discrete Halanay-type inequality and vector Lyapunov methods. In addition, we show that the proposed sufficient condition is independent of the delay parameter. An example is given to demonstrate the effectiveness of the results obtained.

EXISTENCE AND STABILITY OF PERIODIC SOLUTIONS OF DELAYED BAM NEURAL NETWORKS WITH PERIODIC COEFFICIENTS

In this paper, we study the BAM neural networks with variable coefficients and delays. By using the Banach fixed point theorem and constructing suitable Lyapunov function, we obtain some sufficient conditions ensuring the existence, uniqueness and global stability of periodic solution. These results are helpful to design global exponential stable BAM networks and periodic oscillatory BAM networks.

Equilibria and Stability Analysis of Cohen-Grossberg BAM Neural Networks on Time Scale

This paper considers the Cohen-Grossberg BAM neural networks(CG-BAMNNs) on time scale, which can unify and generalize the continuous and discrete systems. First, the criteria for the existence and uniqueness of the equilibrium of CG-BAMNNs are derived on time scale. Then based on that, the authors give the criteria for the stability and estimation of equilibrium of the CG-BAMNNs on time scale. The method proposed in this paper unifies and generalizes the continuous and discrete CGBAMNNs systems, and is applicable to some other neural network systems on time scale with practical meaning. The effectiveness of the proposed criteria for delayed CG-BAMNNs is demonstrated by numerical simulation.

Polynomial dissipativity of proportional delayed BAM neural networks

This paper pays close attention to the global polynomial dissipativity(GPD)for proportional delayed BAM neural networks(PDBAMNNs).The global exponential dissipativity(GED)and the global dissipativity(GD)are also talked about.Under the help of novel Lyapunov functionals and a generalized Halanay inequality,a set of dissipative criteria for such systems are led out,together with the global polynomial attracting set(GPAS)and the global attracting set(GAS).Further,the relationship among GPD,GED and GD is unveiled.Finally,a proposed theoretical condition is validated through a simulation experiment.

Existence and Exponential Stability of Almost Periodic Solution for BAM Neural Networks with Impulse

This paper discusses a class of the bidirectional associative memories（BAM） type neural networks with impulse.By using the Banach fixed point theory and some analysis technology,we obtain the existence of almost periodic solution and stability under some sufficient conditions.

NEW CRITERIA OF EXPONENTIAL STABILITY FOR BAM NEURAL NETWORKS WITH DELAYS

In this Letter, a novel Lyapunov functional is constructed to investigate the exponential stability of the BAM neural networks. New sufficient conditions of the uniqueness and global exponential stability for the equilibrium of BAM neural networks with delays are obtained. The results improve those existing ones.

GLOBAL DYNAMICS OF DELAYED BIDIRECTIONAL ASSOCIATIVE MEMORY (BAM) NEURAL NETWORKS

Without assuming the smoothness,monotonicity and boundedness of the activation functions, some novel criteria on the existence and global exponential stability of equilibrium point for delayed bidirectional associative memory (BAM) neural networks are established by applying the Liapunov functional methods and matrix_algebraic techniques. It is shown that the new conditions presented in terms of a nonsingular M matrix described by the networks parameters,the connection matrix and the Lipschitz constant of the activation functions,are not only simple and practical,but also easier to check and less conservative than those imposed by similar results in recent literature.

具有比例时滞和D算子的BAM神经网络概周期解的存在性与稳定性

为了探讨一类含比例时滞和D算子的中立型BAM神经网络的概周期解的存在性与广义指数稳定性,首先,通过构造概周期函数空间,并利用压缩不动点原理,得到确保系统概周期解存在的充分条件,其次,使用微分不等式技巧得到确保系统概周期解广义指数稳定的充分条件.发现若系统的参数满足一定的条件,那么系统的概周期解是存在的,且是广义指数稳定的,而且所得结果与比例时滞无关.所得结论推进了现有文献中的相关工作.