Search

In this paper, we study the Cohen-Grossberg neural networks with mixed time-varying delays. By applying the Lyapunov functional method and combining with the inequality 3abc ≤ a3 + b3 + c3 (a,b,c > 0) technique, a series of new and useful criteria on the existence of equilibrium point and its global asymptotical stability are established. The results obtained in this paper extend and generalize the corresponding results existing in previous literature. [ABSTRACT FROM AUTHOR]

This paper studies a general class of neural networks with time-varying delays and the neuron activations belong to the set of discontinuous monotone increasing functions. The discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high. Because the delay in combination with high-gain nonlinearities is a particularly harmful source of potential instability, in the paper, conditions which ensure the global convergence of the neural network are derived. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM)-based neural network (NN) approaches have recently gained recognition as unconventional and useful methods for radio frequency (RF) components modeling. In this paper, several improvement techniques including a new data preprocessing technique and an improved training algorithm are presented. Comprehensive cases are compared in this paper. The experimental results indicate that with these techniques, the modified model has better performance. [ABSTRACT FROM AUTHOR]

Code reuse in software reuse has several limitations such as difficulties of understanding and retrieval of the reuse code written by other developers. To overcome these problems, it should be possible to reuse the analysis/design information than source code itself. In this paper, I present analogical matching techniques for the reuse of object models and design patterns. We have suggested the design patterns as reusable components and the representation techniques to store them. The contents of the paper are as follows. 1) Analogical matching functions to retrieve analogous design patterns from reusable libraries. 2) The representation of reusable components to be stored in the library in order to support the analogical matching. [ABSTRACT FROM AUTHOR]

According to analysis of both neuro-electrophysiological experimental data and the biophysical properties of neurons, in early research paper we proposed a new biophysical model that reflects the property of energy coding in neuronal activity. On the based of the above research work, in this paper the proposed biophysical model can reproduce the membrane potentials and the depolarizing membrane current by means of neuro-electrophysiological experimental data. Combination with our previous research results, the proposed biophysical model is demonstrated again to be more effective compared with known biophysical models of neurons. [ABSTRACT FROM AUTHOR]

The existence and global asymptotic stability of a large class of Cohen-Grossberg neural networks is discussed in this paper. Previous papers always assume that the amplification function has positive lower and upper bounds, which excludes a large class of functions. In our paper, it is only needed that the amplification function is positive. Also, the model discussed is general, the method used is direct and the conditions needed are weak. [ABSTRACT FROM AUTHOR]

The paper investigates the existence and global stability of periodic solution of non-autonomous neural networks with delay. Then the existence and uniqueness of periodic solutions of the neural networks are discussed in the paper. Moreover, criterion on stability of periodic solutions of the neural networks is obtained by using matrix function inequality, and algorithm for the criterion on the neural networks is provided. Result in the paper generalizes and improves the result in the existing references. In the end, an illustrate example is given to verify our results. [ABSTRACT FROM AUTHOR]

This paper mainly discusses a generic scheme for on-line adaptive critic design for nonlinear system based on neural dynamic programming (NDP), more exactly, an improved action-depended dual heuristic dynamic programming (ADDHP) method. The principal merit of the proposed method is to avoid the model neural network which predicts the state of next time step, and only use current and previous states in the method, as makes the algorithm more suitable for real-time or on-line application for process control. In this paper, convergence proof of the method will also be given to guarantee the control to reach the optimal. At last, simulation result verifies the performance. [ABSTRACT FROM AUTHOR]

This paper discusses a new application of adaptive dynamic programming (ADP). Meanwhile, traffic control as an important factor in social development is a valuable research topic. Considering with advancement of ADP and importance of traffic control, this paper present a new signal control in a single intersection. Simulation results show that the proposed signal control is valid. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of global asymptoticstability for a class of uncertain neural networks with time-varying and distributed delays. The uncertainties we considered in this paper are norm-bounded, and possibly time-varying. By Lyapunov-Krasovskii functional approach and S-procedure, a new stability criteria for the asymptotic stability of the system is derived in terms of linear matrix inequalities (LMIs). Two simulation examples are given to demonstrate the effectiveness of the developed techniques. [ABSTRACT FROM AUTHOR]

With the vast amount of mobile user information available today, mining knowledge of mobile users is getting more and more important for a mobile commerce (M-commerce) system. Vector space model (VSM) is one of the most popular methods to achieve the above goal. Unfortunately, it can not identify the latent information in the user feature space, which decreases the quality of personalized services. In this paper, we present a concept-lattice based kernel method for mining the hidden user knowledge. The main idea is to employ concept lattice for constructing item proximity matrix, and then embed it into a kernel function, which transforms the original user feature space into a user concept space, and at last, perform personalized services in the user concept space. The experimental results demonstrate that our method is more encouraging than VSM. [ABSTRACT FROM AUTHOR]

This paper attempts to evaluate machine learning based approaches to text categorization including NTC without decomposing it into binary classification problems, and presents another learning scheme of NTC. In previous research on text categorization, state of the art approaches have been evaluated in text categorization, decomposing it into binary classification problems. With such decomposition, it becomes complicated and expensive to implement text categorization systems, using machine learning algorithms. Another learning scheme of NTC mentioned in this paper is unconditional learning where weights of words stored in its learning layer are updated whenever each training example is presented, while its previous learning scheme is mistake driven learning, where weights of words are updated only when a training example is misclassified. This research will find advantages and disadvantages of both learning schemes by comparing them with each other [ABSTRACT FROM AUTHOR]

In this paper, we investigate dynamical behavior of a general class of competitive or cooperative Lotka-Volterra systems with delays. Positive solutions and global stability of nonnegative equilibrium are discussed. Sufficient condition independent of delays guaranteeing existence of globally stable equilibrium is given. A Simulation verifying theoretical results is given, too. [ABSTRACT FROM AUTHOR]

This paper discusses a mathematical model of network, which is more general than the cellular neural networks(CNNs). In this study, we discuss some dynamical properties of this type of network, such as the distribution of equilibrium points and the influence of external input on stability. Moreover, we give some criterions, which ensure the complete stability of this network. [ABSTRACT FROM AUTHOR]

The paper proposed a method for a quadruped robot control system based Central Pattern Generator (CPG) and fuzzy neural networks (FNN). The common approach for the control of a quadruped robot includes two methods mainly. One is the CPG that is based the bionics, the other is the dynamic control that is based the model of quadruped robot. The control result of CPG is decided by the gait data of the quadruped and the parameters of the CPG are choosing manually. Modeling a quadruped robot is difficult because it is a high nonlinear system. This paper presents a much simpler method for the control of a quadruped robot. A simple CPG is adopted for a timing oscillator; it generates the motion periodic pattern of legs. The FNN is used to control the joint motion in order to get a desired stable trajectory motion. [ABSTRACT FROM AUTHOR]

This paper presents a novel fuzzy-neural control algorithm to realize obstacle avoidance of a mobile robot. A heuristic fuzzy-neural network is developed based on heuristic fuzzy rules and the Kohonen clustering network. By applying the off-line and unsupervised training method to this network, the pattern mapping relation between ultrasonic sensory input and velocity command is established. This paper describes mechanical design of the mobile robot, the arrangement of ultrasonic sensors, the obstacle avoidance system based on FKCN, classification of obstacle, the control algorithm for obstacle avoidance and training data library. In order to verify the effectiveness of this algorithm, we give the results of simulation in a computer virtual environment. [ABSTRACT FROM AUTHOR]

This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle; AIA has a strong parallel processing, learning and memorizing ability. This study will design and control a mobile robot within a limited special scale. Through a research method based on the AIA, this study will find out the optimum obstacle-avoiding path. The main purpose of this study is to make it possible for the mobile robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle and best learning efficiency. In the end, through the research method proposed and the experimental results, it will become obvious that the application of the AIA after improvement in the obstacle-avoiding path planning for mobile robots is really effective. [ABSTRACT FROM AUTHOR]

For the practical use of brain-computer interface systems, one of the most significant problems is the generalizing ability of the classifiers, since the states of both people and instruments are altering as time goes on. In this paper, a novel chaotic neural network termed KIII model, is introduced to classify single-trial ECoG during motor imagery, acquired in two different sessions. Then, by comparing with other three traditional classifiers, KIII model shows a greater ability to generalize, which demonstrates that KIII model is an effective tool for brain-computer interfaces systems. [ABSTRACT FROM AUTHOR]

A novel multi-input, multi-output generalized radial basis function (RBF) neural networks for nonlinear system modelling is presented in the paper, which uses extend Kalman filter to sequentially update both the output weights and the centers of the network. Simultaneously, such RBF models employ radial basis functions whose form is determined by admissible exponential generator functions. To test the validity of the proposed method, this paper demonstrates that generalized RBF neural networks with the extended Kalman filter can be used effectively for the identification and modelling of nonlinear dynamical systems. Simulation results reveal that the new generalized RBF networks guarantee faster learning and very satisfactory function approximation capability in modeling nonlinear dynamic systems. [ABSTRACT FROM AUTHOR]

This paper presents an improved neural network model interpretating sonar readings to build occupancy grids of mobile robot. The proposed model interprets sensor readings in the context of their space neighbors and relevant successive history readings simultaneously. Consequently the presented method can greatly weaken the effects by multiple reflections or specular reflection. The output of the neural network is the probability vector of three possible status(empty, occupancy, uncertainty) for the cell. As for sensor readings integration, three probabilities of cell's status are updated by the Bayesian update formula respectively, and the final status of cell is defined by Max-Min principle.Experiments performed in lab environment has shown occupancy map built by proposed approach is more consistent, accurate and robust than traditional method while it still could be conducted in real time. [ABSTRACT FROM AUTHOR]

In the limestone/gypsum wet flue gas desulphurization (FGD) technology, the change of slurry pH value in absorber is a nonlinear and time-variation process with a large number of uncertainties, so it's difficult to acquire satisfying mathematical model. In this paper, a novel multiple improved PIDNN ensemble model is proposed to establish the model of slurry pH value. In this model, the concepts of variable integral and partial differential are introduced in the design of hidden-layer of PIDNN, and the concept of output feedback is utilized to improve the ability of PIDNN for dynamic modeling, then multiple improved PIDNN are dynamic combined to get the system output. The results of simulation with field data of wet FGD indicate the validity of this modeling approach. [ABSTRACT FROM AUTHOR]

In this paper we derive a simple reinforcement learning rule based on a more general form of REINFORCE formulation. We test our new rule on both classification and reinforcement problems. The results have shown that although this simple learning rule has a high probability of being stuck in local optimum for the case of classification tasks, it is able to solve some global reinforcement problems (e.g. the cart-pole balancing problem) directly in the continuous space. [ABSTRACT FROM AUTHOR]

This paper attempts to present a neural inverse control design framework for a class of nonlinear multiple-input multiple-output (MIMO) system with uncertainties. This research effort is motivated by the following considerations: (a) An appropriate reference model that accurately represents the desired system dynamics is usually assumed to exist and to be available, and yet in reality this is not the case often times; (b) In real world applications, there are many cases where controls are constrained within a physically allowable range, which presents another layer of difficulties to directly apply the reference model based inverse control; (c) It is difficult to consider optimal control even for the reference model as in general the analytic solution to the optimal control problem is not available. The simulation study is conducted on a single-machine infinite-bus (SMIB) system to illustrate the proposed design procedure and demonstrates the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive tracking control strategy is established for general non-Gaussian stochastic systems based on two-step neural network models. The objective is to control the conditional PDF of the system output to follow a given target function by using dynamic neural network models. B-spline neural networks are used to model the dynamic output probability density functions (PDFs), then the concerned problem is transferred into the tracking of given weights corresponding to the desired PDF. The dynamic neural networks with undetermined parameters are employed to identify the nonlinear relationships between the control input and the weights. To achieve control objective, an adaptive state feedback controller is given to estimate the unknown parameters and control the nonlinear dynamics. [ABSTRACT FROM AUTHOR]

In this paper, adaptive neural network control is proposed for a class of strict-feedback nonlinear time-delay systems. Unknown smooth function vectors and unknown time-delay functions are approximated by two neural networks, respectively, such that the requirement on the unknown time-delay functions is relaxed. In addition, the proposed systematic backstepping design method has been proven to be able to guarantee semiglobally uniformly ultimately bounded of closed loop signals, and the output of the system has been proven to converge to a small neighborhood of the desired trajectory. Finally, simulation result is presented to demonstrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

This paper proposes ANFIS logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude of the air vehicle is controlled. The implementation framework utilizes MATLAB's standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed 6 degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. The Aerosonde UAV model is used in the simulations in order to demonstrate the performance and the potential of the controllers. Additionally, Microsoft Flight Simulator and FlightGear Flight Simulator are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance. [ABSTRACT FROM AUTHOR]

Coronary heart disease (CHD) is a serious disease causing more and more morbidity and mortality. Combining western medicine and Traditional Chinese Medicine (TCM) to heal CHD becomes especially necessary for medical society today. Since western medicine faces some problems, like high cost and more side effects. TCM can be a complementary alternative to overcome these defects. Identification of what syndrome a CHD patient caught has been a challenging issue for medical society because the core of TCM is syndrome. In this paper, we carry out a large-scale clinical epidemiology to collect data with 1069 cases, each of which must be a CHD instance but may be diagnosed as different syndromes. We take blood stasis syndrome (frequency is 69%) as an example, employ four distinct kinds of data mining algorithms: Bayesian model; Neural Network; Support vector machine and Decision trees to classify the data and compare their performance. The results indicated that neural network is the best identifier with 88.6% accuracy on the holdout samples. The next is support vector machine with 82.5% accuracy, a slight higher than Bayesian model with 82.0% counterpart. The decision tree performs the worst, only 80.4%. We conclude that in identifying syndromes in CHD, neural network can provide a best insight to clinical application. [ABSTRACT FROM AUTHOR]

This paper puts forward a multi-stages competitive neural networks approach for motion trajectory pattern analysis and learning. In this method, the rival penalized competitive learning method, which could well overcome the competitive networks' problems of the selection of output neurons number and weight initialization, is used to discover the distribution of the flow vectors according to the trajectories' time orders. The experiments on different sites with CCD and infrared cameras demonstrate that our method is valid for motion trajectory pattern learning and can be used for anomaly detection in outdoor scenes. [ABSTRACT FROM AUTHOR]

In the image registration process, there always exists rotation transformation. The ordinary methods such as NCC (Normalized Cross Correlation Algorithm), SD (Square Difference Algorithm), SSDA (Sequential Similarity Detection Algorithm), are not suitable for rotated image registration. In this paper, a method based on circular template, intensity distribution and SD is proposed for rotation image registration. Through the CPs (Control Points) got by the proposed method, transformation model and least square method, the rotation parameters are obtained. Experimental results verify its effectiveness. Compared with the existing feature-based approaches, it is easier to obtain CPs and needs no salient objects. [ABSTRACT FROM AUTHOR]

In this paper, we present two novel memetic algorithms (MAs) for gene selection. Both are synergies of Genetic Algorithm (wrapper methods) and local search methods (filter methods) under a memetic framework. In particular, the first MA is a Wrapper-Filter Feature Selection Algorithm (WFFSA) fine-tunes the population of genetic algorithm (GA) solutions by adding or deleting features based on univariate feature filter ranking method. The second MA approach, Markov Blanket-Embedded Genetic Algorithm (MBEGA), fine-tunes the population of solutions by adding relevant features, removing redundant and/or irrelevant features using Markov blanket. Our empirical studies on synthetic and real world microarray dataset suggest that both memetic approaches select more suitable gene subset than the basic GA and at the same time outperforms GA in terms of classification predictions. While the classification accuracies between WFFSA and MBEGA are not significantly statistically different on most of the datasets considered, MBEGA is observed to converge to more compact gene subsets than WFFSA. [ABSTRACT FROM AUTHOR]

In this paper, we propose a probabilistic approach to feature selection for multi-class text categorization. Specifically, we regard document class and occurrence of each feature as events, calculate the probability of occurrence of each feature by the theorem on the total probability and utilize the values as a ranking criterion. Experiments on Reuters-2000 collection show that the proposed method can yield better performance than information gain and χ-square, which are two well-known feature selection methods. [ABSTRACT FROM AUTHOR]

Radar high-resolution range profile (HRRP) has received intensive attention from the radar automatic recognition (RATR) community. Since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity, only the amplitude information in the complex HRRP, what is called the real HRRP, is used for RATR. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector contains the difference phase information between range cells but no initial phase information in the complex HRRP. The recognition algorithms, frame-template-database establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are proper. [ABSTRACT FROM AUTHOR]

This paper presents a computational intelligence approach for predicting missing data in the presence of concept drift using an ensemble of multi-layered feed forward neural networks. An algorithm that detects concept drift by measuring heteroskedasticity is proposed. Six instances prior to the occurrence of missing data are used to approximate the missing values. The algorithm is applied to simulated time series data sets resembling non-stationary data from a sensor. Results show that the prediction of missing data in non-stationary time series data is possible but is still a challenge. For one test, up to 78% of the data could be predicted within 10% tolerance range of accuracy. [ABSTRACT FROM AUTHOR]

Network anomaly detection has been a hot topic in the past years. However, high false alarm rate, difficulties in obtaining exact clean data for the modeling of normal patterns and the deterioration of detection rate because of "unclean" training set always make it not as good as we expect. Therefore, we propose a novel data mining method for network anomaly detection in this paper. Experimental results on the well-known KDD Cup 1999 dataset demonstrate it can effectively detect anomalies with high true positives, low false positives as well as with high confidence than the state-of-the-art anomaly detection methods. Furthermore, even provided with not purely "clean" data (unclean data), the proposed method is still robust and effective. [ABSTRACT FROM AUTHOR]

The problem of fault diagnosis of electrical machine has been an ongoing research in power systems. Many machine learning tools have been applied to this problem using static machine learning structures such as neural network, support vector machine that are unable to accommodate new information as it becomes available into their existing models. This paper presents a new method to bushing fault condition monitoring using fuzzy ARTMAP(FAM). FAM is introduced for bushing condition monitoring because it has the ability to incrementally learn information as it becomes available. An ensemble of classifiers is used to improve the classification accuracy of the systems. The testing results show that FAM ensemble gave an accuracy of 98.5%. Furthermore, the results show that fuzzy ARTMAP can update its knowledge in an incremental fashion without forgetting previously learned information. [ABSTRACT FROM AUTHOR]

In this paper we apply harmony data smoothing learning on a weighted kernel density model to obtain a sparse density estimator. We empirically compare this method with the least squares cross-validation (LSCV) method for the classical kernel density estimator. The most remarkable result of our study is that the harmony data smoothing learning method outperforms LSCV method in most cases and the support vectors selected by harmony data smoothing learning method are located in the regions of local highest density of the sample. [ABSTRACT FROM AUTHOR]

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied traing the weights of recurrent neural networks for nonlinear system identification. Both hidden layers and output layers can be updated. From a dynamic systems point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. A simple simulation gives the effectiveness of the suggested algorithm. [ABSTRACT FROM AUTHOR]

Multiple-Instance Learning is increasingly becoming one of the most promiscuous research areas in machine learning. In this paper, a new algorithm named NRBF-MI is proposed for Multi-Instance Learning based on normalized radial basis function network. This algorithm defined Compact Neighborhood of bags on which a new method is designed for training the network structure of NRBF-MI. The behavior of kernel function radius and its influence is analyzed. Furthermore a new kernel function is also defined for dealing with the labeled bags. Experimental results show that the NRBF-MI is a high efficient algorithm for Multi-Instance Learning. [ABSTRACT FROM AUTHOR]

Investigated in this paper are the uniform approximation capabilities of sum-of-product (SOPNN) and sigma-pi-sigma (SPSNN) neural networks. It is proved that the set of functions that are generated by an SOPNN with its activation function in C(ℝ) is dense in $C(\mathbb{K})$ for any compact $\mathbb{K}\in \mathbb{R}^N$, if and only if the activation function is not a polynomial. It is also shown that if the activation function of an SPSNN is in C(ℝ), then the functions generated by the SPSNN are dense in $C(\mathbb{K})$ if and only if the activation function is not a constant. [ABSTRACT FROM AUTHOR]

Multiple-instance learning (MIL) is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. In this paper a novel algorithm has been introduced for multiple-instance learning. This method was inspired by both diverse density (DD) and its expectation maximization version (EM-DD). It converts MIL problem to a single-instance setting. This improved method has better accuracy and time complexity than DD and EM-DD. We apply it to drug activity prediction and image retrieval. The experiments show it has competitive accuracy values compared with other previous approaches. [ABSTRACT FROM AUTHOR]

This paper considers the problem of finite-time boundedness (FTB) of the general delayed neural networks with norm-bounded parametric uncertainties. The concept of FTB for time delay system is extended first. Then, based on the Lyapunov function and linear matrix inequality (LMI) technique, some delay-dependent criteria are derived to guarantee FTB. The conditions can be reduced to a feasibility problem involving linear matric inequalities (LMIs). Finally, two examples are given to demonstrate the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

Online-sequential extreme learning machine (OS-ELM) shows a good solution to online learning using extreme learning machine approach for single-hidden-layer feedforward network. However, the algorithm tends to be data-dependent, i.e. the bias values need to be adjusted depending on each particular problem. In this paper, we propose an enhancement to OS-ELM, which is referred to as robust OS-ELM (ROS-ELM). ROS-ELM has a systematic method to select the bias that allows the bias to be selected following the input weights. Hence, the proposed algorithm works well for every benchmark dataset. ROS-ELM has all the pros of OS-ELM, i.e. the capable of learning one-by-one, chunk-by-chunk with fixed or varying chunk size. Moreover, the performance of the algorithm is higher than OS-ELM and it produces a better generalization performance with benchmark datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a novel artificial neural oscillator consisted of two neurons with excellent control properties. The mutual connections between the neurons are just linear functions and determine the oscillation angular frequency. And each neuron has a nonlinear self-feedback connection to hold up oscillation amplitude. The dynamics of the neural oscillator was modelled with nonlinear coupling functions. And the stability, amplitude, angular frequency of the oscillator are determined independently by three parameters of the functions. Since it has simple structure and favorable control advantages, it can be used in bionic robot's locomotion control system. The first application is an artificial central pattern generator (CPG) controller for bionic robot's joint. The second is a bionic neural network for fish-robot's locomotion control. [ABSTRACT FROM AUTHOR]

In this paper, the global uniform asymptotical stability is studied for neural networks with multiple time-varying delays by constructing appropriate Lyapunov-Krasovskii functional and using the linear matrix inequality (LMI) approach. The restriction on the derivative of the time-varying delay function τij(t) to be less than unit is removed by using slack matrix method. A numerical example is provided to demonstrate the effectiveness and applicability of the proposed criteria. [ABSTRACT FROM AUTHOR]

In this paper, the difference between using the inverted neuron output and negative resistor for expressing inhibitory synapse is studied. We analyzed that the total conductance seen at the neuron input is different in these two methods. And this total conductance has been proved to effect on the system stability in this paper. Also, we proposed the method how to stabilize the input space and improve the system's performance by adjusting the input conductance between neuron input and ground. Pspice is used for circuit level simulation. [ABSTRACT FROM AUTHOR]

In this paper, the global asymptotic and exponential stability are investigated for a class of neural networks with distributed time-varying delays. By using appropriate Lyapunov-Krasovskii functional and linear matrix inequality (LMI) technique, two delay-dependent sufficient conditions in LMIs form are obtained to guarantee the global asymptotic and exponential stability of the addressed neural networks. The proposed stability criteria do not require the monotonicity of the activation functions and the differentiability of the distributed time-varying delays, which means that the results generalize and further improve those in the earlier publications. An example is given to show the effectiveness of the obtained condition. [ABSTRACT FROM AUTHOR]

The global asymptotic stability of discrete-time Cohen-Grossberg neural networks (CGNNs) with or without time delays is studied in this paper. The CGNNs are transformed into discrete-time interval systems, and several sufficient conditions of asymptotic stability for these interval systems are derived by constructing some suitable Lyapunov functionals. The obtained conditions are given in the form of linear matrix inequalities that can be checked numerically and very efficiently by resorting to the MATLAB LMI Control Toolbox. [ABSTRACT FROM AUTHOR]

In this paper, a class of generalized nonautonomous cellular neural networks with time-varying delays are studied. By means of Lyapunov functional method, improved Young inequality ambn ≤ mat− n + nbtm (0 ≤ m ≤ 1, m + n = 1,t > 0) and the homeomorphism theory, several sufficient conditions are given guaranteeing the existence, uniqueness and global exponential stability of the equilibrium point. The proposed results generalize and improve previous works. An illustrative example is also given to demonstrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, the global asymptotic stability of Hopfield neural networks with time delays is investigated. Some novel sufficient conditions are presented for the global stability of a given delayed Hopfield neural networks by constructing Lyapunov functional and using some well-known inequalities. A linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the given neural networks. An illustrative example is provided to demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]