STABILITY CONTROL AND ANALYSIS OF ASYMMETRIC CONTINUOUS NEURAL NETWORKS

A new method for analyzing dynamics of continuous neural networks is proposed,and the necessary convergence conditions for a class of associative networks are obtained. Basedon the stability criterion and the equations of equilibrium set of the network, synthesis of aclass of associative neural networks is given. The stability control model of asymmetric unstablenetworks is suggested, which is also a valid way for optimization and dynamic control of stableneural networks.     

STABILITY OF BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DELAYS

In this paper the globally asymptotic stability of more general two-layer nonlinear feedback associative memory neural networks with time delays is examined. The sufficient conditions of existence, uniqueness and globally asymptotic stability of the equilibrum position are given. Finally, two interesting examples to illustrate the theory are given.     

神经网络随机扰动稳定性研究

本文基于随机微分方程理论,严格地分析了一类广义的Hopfield连续时间神经网络在白噪声扰动下的稳定性,并建立了相应的稳定性判据和网络的设计准则。     

Testability of artificial neural networks: A behavioral approach

Testability analysis of neural architectures can be performed at a very high abstraction level on the computational paradigm. In this paper, we consider the case of feed-forward multi-layered neural networks. We introduce a behavioral error model which allows good mapping of the physical faults in widely different implementations. Conditions for error controllability, observability and global testability are analytically derived; their purpose is that of verifying whether it is possible to excite all modeled errors and to propagate the error's effects to the primary outputs (actual test vectors being then technological-dependent). Mapping of physical faults onto behavioral errors is performed for some representative, architectures.     

DYNAMIC PROPERTIES OF CONTINUOUS ASYMMETRIC NEURAL NETWORKS

The dynamic properties of continuous asymmetric neural networks are discussed in this paper. The condition in the existence of unique equilibrium point is obtained. It is also dealt with the conditions in not producing static bifurcation and Hopf's bifurcation and is put forward the sufficient conditions for overall asymptotic stability and exponential stability.     

A low-power synapse/neuron cell for artificial neural networks

A reconfigurable low-voltage low-power cell that can function either as a synapse or a neuron is proposed and analyzed in this article for the VLSI implementation of artificial neural networks (ANNs). The measured results are also presented. The design is based on the current-mode approach and uses the square-law characteristics of an MOS transistor working in saturation. The proposed fabricated synapse/neuron cell utilizes I–V converters, current mirror, and a ±1 V power supply to achieve superior performance. Modularity, ease of interconnectivity, expandability and reconfigurability are the main advantages of this cell.     

QUALITATIVE ANALYSIS OF BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS

In this paper, the global exponential stability of an equilibrium position for general bidirectional associative memory neural networks are studied. The sufficient conditions of existence and uniqueness of the equilibrium position are given. The method of energy function is examined. Two examples are given to illustrate the theory.     

ON THE STABILITY OF CELLULAR NEURAL NETWORKS WITH FEEDBACK MODE

Cellular Neural Networks (CNN) with feedback mode and M×N cells are equivalent to a network which possesses 2M×N cells, a neighborhood with mirror-like structure, space-variant templates and without feedback as well as without input templates. The stability of the CNN with feedback mode and transformations with the neighborhood of mirror-like structure are discussed.     

LOGICAL ANALYSIS FOR NEURAL NETWORKS

By comparison with constraint satisfaction networks, this paper presents an essential frame of the logical theory for continuous-state neural networks, and gives the quantitative analyzing method for contradiction. The analysis indicates that the basic reason for the alternation of the logical states of the neurons is the existence of superior contradiction inside the networks. The dynamic process for a neural network to find a solution corresponds to eliminating the superior contradiction.     

具有时滞的细胞神经网络的稳定性

该文研究了具有时滞的细胞神经网络的稳定性问题,运用Lyapunov泛函法和Razumikhin法分别给出了时滞细胞神经网络全局渐近稳定的两个新的充分条件。其中,第一个条件与时延无关,而第二个条件与时延有关。获得的定理推广了已有文献中的结果,对于时滞细胞神经网络的硬件设计具有一定的指导意义。     

DISTRIBUTED PARAMETER NEURAL NETWORKS FOR SOLVING PARTIAL DIFFERENTIAL EQUATIONS

Novel distributed parameter neural networks are proposed for solving partial differential equations, and their dynamic performances are studied in Hilbert space. The locally connected neural networks are obtained by separating distributed parameter neural networks. Two simulations are also given. Both theoretical and computed results illustrate that the distributed parameter neural networks are effective and efficient for solving partial differential equation problems.     

基于Hopfield神经网络车辆路径的优化研究

针对车辆路径的优化问题，借鉴旅行商路径问题的解决，利用Hopfield神经网络，结合电子地图和交通路况的一些人为因素（禁行、单行道等），提出了车辆行驶接近最优路径的算法和参数的学习方法。     

On the predictability of next generation mobile network traffic using artificial neural networks

Though the introduction of the new 4th Generation mobile access technologies promises to satisfy the increasing bandwidth demand of the end‐users, it poses in parallel the need for novel resource management approaches at the side of the base station. To this end, schemes that try to predict the forthcoming bandwidth demand using supervised learning methods have been proposed in the literature. However, there are still open issues concerning the training phase of such methods. In the current work, the authors propose a novel scheme that dynamically selects a proper training set for artificial neural network prediction models, based on the statistical characteristics of the collected data. It is demonstrated that an initial statistical processing of the collected data and the subsequent selection of the training set can efficiently improve the performance of the prediction model. Finally, the proposed scheme is validated using network traffic collected by real, fully operational base stations. Copyright © 2013 John Wiley & Sons, Ltd.     

离散Hopfield神经网络的稳定性研究

离散Hopfield神经网络的稳定性是网络应用的基础.文中主要研究非对称离散Hopfield神经网络的异步、同步、部分同步演化方式的稳定性,并给出了一些新的稳定性条件,所获结果推广了一些已有的结论.     

Comparing the efficiency of artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics

Surface Electromyography (sEMG) plays a key role in many applications such as control of Human-Machine Interfaces (HMI) and neuromusculoskeletal modeling. It has strongly nonlinear relations to joint kinematics and reflects the subjects’ intention in moving their limbs. Such relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification approaches. However, these methods have drawbacks that limit their usability. Different from them, joint kinematics can be continuously reconstructed from sEMG via estimation approaches, for instance, the Artificial Neural Networks (ANNs). The Comparison of different ANNs used in different studies is difficult, and in many cases, impossible. The current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG signals. The presented ANNs are Feedforward, Cascade-Forward, Radial Basis Function (RBFNN), and Generalized Regression (GRNN). Each ANN is applied to its special parametric study. All the methods efficiently solved the regression problem of the complex multi-input multi-output bio-system. The RBFNN has the best performance over the others with a 79.80% mean correlation coefficient over all joints, and its accuracy reaches as high as 92.67% in some joints. Interestingly, the highest accuracy over individual joints is 93.46%, which is achieved via the GRNN. The good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.     

Pole discontinuity removal using artificial neural networks for microstrip antenna design

This article presents the use of artificial neural networks for the evaluation of integrals with finite number of pole singularities while formulating the integral equation for the electric surface current density. A feed-forward single-layer back-propagated artificial neural network (ANN) model has been trained to approximate the discontinuous integrand function. Generation of a soft continuous function obtained from the ANN model and closed-loop expressions for the evaluation of the integrals are presented. The proposed technique is applied to compute the input impedance of microstrip antenna and results have been compared with IE3D. Integration has been performed using n-point Gaussian quadrature rule for evaluating the reaction matrix.     

Microwave FinFET modeling based on artificial neural networks including lossy silicon substrate

Nowadays, FinFET represents a new and promising transistor structure for the aggressive downscaling of the CMOS technology. Typically, the small-signal modeling for FinFET is based on compact models or on equivalent circuit representations. As an alternative to such approaches, a small-signal behavioral model based on artificial neural networks is developed in this paper. Particular attention is devoted to modeling the low-frequency kinks of the scattering parameters, due to the lossy silicon substrate. The model is efficient and accurate, as confirmed by the comparison between measured and simulated microwave behavior.     

A unified mobility model for analysis and simulation of mobile wireless networks

We propose a novel mobility model, named Semi-Markov Smooth (SMS) model, to characterize the smooth movement of mobile users in accordance with the physical law of motion in order to eliminate sharp turns, abrupt speed change and sudden stops exhibited by existing models. We formulate the smooth mobility model by a semi-Markov process to analyze the steady state properties of this model because the transition time between consecutive phases (states) has a discrete uniform distribution, instead of an exponential distribution. Through stochastic analysis, we prove that this model unifies many good features for analysis and simulations of mobile networks. First, it is smooth and steady because there is no speed decay problem for arbitrary starting speed, while maintaining uniform spatial node distribution regardless of node placement. Second, it can be easily and flexibly applied for simulating node mobility in wireless networks. It can also adapt to different network environments such as group mobility and geographic constraints. To demonstrate the impact of this model, we evaluate the effect of this model on distribution of relative speed, link lifetime between neighboring nodes, and average node degree by ns-2 simulations.

二进神经网络的模式匹配学习

二进神经网络的知识提取需要了解每个神经元的逻辑意义。一般来说,对二进神经网络学习结果的分析是困难的。该文提出了一种基于线性可分结构系结构分析的学习算法,采用这种方法对布尔空间的样本集合进行学习,得到的二进神经网络隐层神经元都归属于一类或几类线性可分结构系,只要这几类线性可分结构系的逻辑意义是清晰的,就可以分析整个学习结果的知识内涵。     

具有参数摄动的时滞Hopfield神经网络的鲁棒稳定性

 研究一类具有参数摄动的时滞Hopfield神经网络模型的鲁棒稳定性.应用Lyapunov泛函法,给出了平衡点渐近稳定的充分条件.利用矩阵范数的性质及线性矩阵不等式(LMI)理论,又得到了两个便于计算和验证的推论.提供了一种估计网络渐近稳定平衡点吸引域的方法,并详尽地分析了吸引域对神经网络实现联想记忆的影响.数值例子进一步证明了结论的有效性.