Comments on "Stochastic choice of basis functions in adaptive function approximation and the functional-link net" [and reply] [0.03%]
关于“自适应函数逼近中基函数的随机选择及功能链接网络”的评论[及回复]
J Y Li,W S Chow,B Igelnik et al.
J Y Li et al.
This paper includes some comments and amendments of the above-mentioned paper by Igelnik et al. (1995). Subsequently, Theorem 1 in the above-mentioned paper has been revised. The significant change of the original theorem is the space of th...
On the "Identification and control of dynamical systems using neural networks" [0.03%]
基于神经网络的动态系统辨识与控制研究
E Rios-Patron,R D Braatz
E Rios-Patron
Referring to the above said paper by Narendra-Parthasarathy (ibid., vol.1, p4-27 (1990)), it is noted that the given Example 2 (p.15) has a third equilibrium state corresponding to the point (0.5, 0.5).
A Atiya,C Ji
A Atiya
Generalization is one of the most important problems in neural-network research. It is influenced by several factors in the network design, such as network size, weight decay factor, and others. We show here that the initial weight distribu...
Discrete-time convergence theory and updating rules for neural networks with energy functions [0.03%]
离散时间收敛理论及具有能量函数的神经网络的更新规则
L Wang
L Wang
We present convergence theorems for neural networks with arbitrary energy functions and discrete-time dynamics for both discrete and continuous neuronal input-output-functions. We discuss systematically how the neuronal updating rule should...
A binary Hopfield neural-network approach for satellite broadcast scheduling problems [0.03%]
一种用于卫星广播调度问题的二值Hopfield神经网络方法研究
N Funabiki,S Nishikawa
N Funabiki
This paper presents a binary Hopfield neural network approach for finding a broadcasting schedule in a low-altitude satellite system. Our neural network is composed of simple binary neurons on the synchronous parallel computation, which is ...
A simplification of the backpropagation-through-time algorithm for optimal neurocontrol [0.03%]
简化通过时间反向传播算法以实现最优神经控制
H Bersini,V Gorrini
H Bersini
Backpropagation-through-time (BPTT) is the temporal extension of backpropagation which allows a multilayer neural network to approximate an optimal state-feedback control law provided some prior knowledge (Jacobian matrices) of the process ...
S R Hasan,N K Siong
S R Hasan
In this paper emerging parallel/distributed architectures are explored for the digital VLSI implementation of adaptive bidirectional associative memory (BAM) neural network. A single instruction stream many data stream (SIMD)-based parallel...
Toward a general-purpose analog VLSI neural network with on-chip learning [0.03%]
具有片上学习功能的通用模拟VLSI神经网络研究
A J Montalvo,R S Gyurcsik,J J Paulos
A J Montalvo
This paper describes elements necessary for a general-purpose low-cost very large scale integration (VLSI) neural network. By choosing a learning algorithm that is tolerant of analog nonidealities, the promise of high-density analog VLSI is...
Iterative generation of higher-order nets in polynomial time using linear programming [0.03%]
利用线性规划在多项式时间内迭代生成高阶网络
A Roy,S Mukhopadhyay
A Roy
This paper presents an algorithm for constructing and training a class of higher-order perceptrons for classification problems. The method uses linear programming models to construct and train the net. Its polynomial time complexity is prov...