论并行神经计算机系统设计的一些问题

神经计算机的研究是神经网络研究中的一项重要内容,神经计算机就是指根据神经网络结及其计算特点,用电子器件、光学器件或分子/化学器件而构成的计算系统.神经计算机的研究主集中在两个方面,即器件研究及系统构造.神经计算机的研究也称为神经网络实现技术的研究。     

神经网络计算及其在薄板弯曲分析中的应用

利用动态学习率改进ＢＰ算法,建立基于混合神经网络的计算力学理论,为结构分析提供一种新的算法。数值模拟结果表明,该方法具有良好的应用前景。     

解决陷入局部极小的一种新方法

本文提出一种能有效。解决Ｈｏｐｆｉｅｌｄ网在能量变。过程之中陷入局部极小问题的方法，这种方法通过有选择性地改变权值矩阵Ｗ的对角元以及有某种特定顺序的串行工作方式来使得网络跳出局部极小值（点）向能量最小点逼近，模拟结果显示；这种方法不仅速度比较快（与模拟退火法ＳｉｍｕｌａｔｅｄＡｎｎｅａｌｉｎｇＭｅｔｈｏｄ比较），而且在每次实验中达到全局最小点的成功率也非常高．在所进行的众多的实验中，迄今为止，尚未出现不成功的情况．     

Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities

In this paper, we present the architecture and describe the functionality of an Intelligent Tutoring System (ITS), which uses an expert system to make decisions during the teaching process. The expert system uses neurules for knowledge representation of the pedagogical knowledge. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The expert system consists of three components: the user modelling unit, the pedagogical unit and the inference system. The pedagogical knowledge is distributed in a number of neurule bases within the user modelling and the pedagogical unit. Another important component of the ITS, for both its development and maintenance, is its knowledge management unit, which provides knowledge acquisition and knowledge update capabilities to the system, that is, offers expert knowledge authoring capabilities to the system.     

Signal processing and time series description: A Perspective of Computational Intelligence and Granular Computing

This study provides a general introduction to the principles, algorithms and practice of Computational Intelligence (CI) and elaborates on their use to signal processing and time series. In this setting, we discuss the main technologies of Computational Intelligence (namely, neural networks, fuzzy sets or Granular Computing, and evolutionary optimization), identify their focal points and stress an overall synergistic character, which ultimately gives rise to the highly synergistic CI environment. Furthermore, the main advantages and limitations of the CI technologies are discussed. In the sequel, we present CI-oriented constructs in signal modeling, classification, and interpretation.     

Corrigendum to “Exponential state estimation for recurrent neural networks with distributed delays” [Neurocomputing 71(1–3) (2007) 428–438]

In the letter [Neurocomputing 71(1–3) (2007) 428–438], there exists one minor error in computing the derivative of V₂((t)) and thus, the proof of Theorem 1 needs some improvement.