基于动态递归网络PID自适应控制器设计

本文分析了改进的ELMAN网络的结构,并讨论了神经网络的学习算法,针对BP算法的缺陷,提出了用遗传算法修正网络权值的学习算法.本文不仅将采用遗传算法进行训练的改进ELMAN网络应用于电阻炉加热系统的建模,而且针对该系统的特点提出了一种带预测模型的神经网络PID自适应控制器,并最后将该控制器应用于电阻炉温度控制,取得了良好的控制效果.     

基于动态递归网络的PID自适应控制器的设计与应用

本文分析了改进的ELMAN网络的结构,并讨论了神经网络的学习算法,针对BP算法的缺陷,提出了用遗传算法修正网络权值的学习算法。本文不仅将采用遗传算法进行训练的改进ELMAN网络应用于汽车磷化加热系统的建模,而且针对该系统的特点提出了一种带预测模型的神经网络PID自适应控制器,并最后将该控制器应用于磷化温度控制,取得了良好的控制效果。     

基于改进微粒群算法的Elman网络在非线性动态系统辨识中的应用

针对反向传播算法优化ELMAN神经网络权值时存在的收敛速度慢,求解精度低等问题,提出了改进微粒群算法修正网络权值的学习算法.仿真结果验证了基于改进微粒群算法的Elman神经网络在非线性系统中进行辨识的可行性。     

基于改进遗传算法的神经网络模型辨识

利用前向神经网络对非线性动态系统建模时存在着很大的缺陷，因此提出采用递归网络(RNN)对非线性动态系统建模。并在权值的修正上，摆脱常用BP算法的束缚，采用改进的遗传算法搜索最优权值。最后对一高阶非线性系统进行建模仿真实验，结果表明该方法是有效的。     

基于时变RBF网络的非线性时变系统建模

在常规RBF神经网络中采用时变权值,将其应用于非线性时变系统的建模。采用减聚类算法确定网络隐含层神经元数与基函数中心参数,以迭代学习最小二乘算法修正神经网络时变权值,给出时变RBF网络的学习算法。分析表明,迭代学习最小二乘权值修正算法保证了网络时变权值的有界性,迭代误差收敛于零。仿真结果验证了该方法在非线性时变系统建模方面的有效性。     

基于模糊分类的模糊神经网络辨识方法及应用

基于改进的T-S模型，提出一种自适应模糊神经网络模型(AFNN)，给出了网络的连接结构和学习算法。基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。利用卡尔曼滤波算法在线辨识删的后件参数。AFNN结构简洁，逼近能力强，能够显著提高辨识精度，并且在线辨识的模糊模型简单有效。将该AFNN用于非线性系统的模糊辨识和化工过程连续搅拌反应器(CSTR)的建模中，仿真结果验证了该方法的有效性，表明该网络能够实现复杂非线性系统的建模，而且建模精度高、收敛速度快。可当作复杂系统建模的一种有效手段。     

模糊灰色认知网络的建模方法及应用

针对具有不确定性非线性系统的机理模型难以建立的问题,提出了基于模糊灰色认知网络（Fuzzy grey cognitive networks,FGCN）的非线性系统建模方法.该方法将模糊认知网络和灰色系统理论相结合,把模糊认知网络的节点状态值和权值扩展为灰色区间,引入灰度来评判可靠性.采用一种带终端约束的非线性Hebbian学习算法（Nonlinear hebbian learning,NHL）辨识FGCN的模型参数,引入了与FGCN模型中节点的系统实际测量值对应的灰数值,在更新机制中增加了包含系统测量值与预测值之差的修正项,对权值进行有监督的修正.利用水箱控制系统进行的仿真实验结果表明,本文提出的建模方法能解决对数据存在不确定性或缺失的复杂系统建模的难题,所建的模型能做出接近人类智能的控制决策,所采用的权值学习方法具有收敛速度快、学习结果精准等优点,并克服了传统非线性Hebbian算法对初始值依赖性强的缺点,对不确定性系统的建模具有广泛适用性.     

基于改进的DeepESN软测量建模方法及应用

针对硫回收装置中硫化氢和二氧化硫浓度的实时监控预测问题,提出一种基于改进的深度回声状态网络(DeepESN)软测量建模方法,给出了其离线学习算法.改进的DeepESN网络能够通过多层回声状态网络的结构,可以对具有强非线性特性的化工过程进行有效的深度学习和预测.离线学习算法在求输出权值时加入了岭回归算法,有效地提高了网络学习的稳定性.将该方法在同等条件下与现有的软测量建模方法进行了比较,基于改进的DeepESN软测量建模方法具有更好的学习能力、更高的学习效率和预测精度.     

用神经网络辨识非线性大滞后系统的研究

本文主要研究非线性大滞后系统的建模问题， 提出用串级网络模型进行滞后补偿，并给出了网络模型权系数的学习算法．     

系统组卷算法的研究

针对当前计算机网络考试系统的相关技术展开研究。主要对系统组卷算法进行了重点分析和研究，从如何选取遗传算法出发．选择基于遗传算法的组卷算法进行设计使其更适用于计算机网络考试系统。     

Identification of time-series signals using a dynamic neural network with GA-based training

We propose a dynamic neural network (DNN) that realizes a dynamic property and has a network structure with the properties of inertia, viscosity, and stiffness without time-delayed input elements, and a training algorithm based on a genetic algorithm (GA). In a previous study, we proposed a modified training algorithm for the DNN based on the error back-propagation method. However, in the previous method it was necessary to determine the values of the DNN property parameters by trial and error. In the newly proposed DNN, the GA is designed to train not only the connecting weights but also the property parameters of the DNN. Simulation results show that the DNN trained by the GA obtains good performance for time-series patterns generated from an unknown system, and provides a higher performance than the conventional neural network. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, 0ita, Japan, February 4–6, 2005     

Fuzzy neural networks-based quality prediction system for sinteringprocess

A hybrid fuzzy neural networks and genetic algorithm (GA) system is proposed to solve the difficult and challenging problem of constructing a system model from the given input and output data to predict the quality of chemical components of the finished sintering mineral. A bidirectional fuzzy neural network (BFNN) is proposed to represent the fuzzy model and realize the fuzzy inference. The learning process of BFNN is divided into off-line and online learning. In off-line learning, the GA is used to train the BFNN and construct a system model based on the training data. During online operation, the algorithm inherited from the principle of backpropagation is used to adjust the network parameters and improve the system precision in each sampling period. The process of constructing a system model is introduced in details. The results obtained from the actual prediction demonstrate that the performance and capability of the proposed system are superior     

基于动态递归反馈型神经网络的永磁同步电机 转矩观测器设计

现有永磁同步电机普遍存在算法复杂、电机参数辨识困难、电磁转矩难以通过数学模型来精 确估算等问题,从而导致电机控制精度以及驱动系统的整体性能下降。该研究设计了一种基于动态递 归反馈型神经网络的电机电磁转矩网络拓扑模型,使用 MATLAB/Simulink 将该神经网络封装成转矩观 测器,并用于电机转矩的精确估算。实验结果显示,与传统转矩和反向传播神经网络计算方式相比, 该研究所设计的转矩观测器具有更高的转矩计算精度,与反向传播神经网络算法相比具有更高的控制 精度与准确性。     

A fast parameter estimation for nonlinear multi-regressions based on the Choquet integral with quantum-behaved particle swarm optimization

In general, the inherent interaction among attributes must be considered circumspectly in the study of data mining and information fusion. A nonlinear model with a nonlinear multi-regression model based on the Choquet integral (NMRCI) is suitable for dealing with these problems. However, this NMRCI is an over-determined system and it is difficult to find the analytic solution. Hence, many researchers have proposed many algorithms: namely, the genetic algorithm, the neural network, particle swarm optimization, quantum-behaved particle swarm optimization (QPSO), etc., to estimate the parameters of NMRCI. In this study, a modified QPSO (MQPSO) algorithm, which is used to estimate the parameters of NMRCI, is proposed. That is, the proposed MQPSO algorithm applies the concept of the GA to the QPSO algorithm so that it can improve the convergent speed and conquer the phenomenon of premature. From the simulation results, the proposed MQPSO gives a more precise estimation and faster convergent speed for the estimated parameters of NMRCI.     

一种优化神经网络的教与学优化算法

为了提高BP神经网络的输出精度,提出一种改进的教与学优化算法进行神经网络中的权值和阈值的优化调整.算法对基本的教与学优化算法的“教”阶段和“学”阶段分别进行改进,并提出一种“自学”机制来增强算法的学习能力.通过函数拟合实验和拖拉机齿轮箱故障诊断实验进行算法性能测试,结果表明,与遗传算法和基本的教与学优化算法相比,该算法具有收敛速度快、求解精度高等优势.     

多约束选址—路径问题的改进混合遗传算法研究

选址—路径问题(LRP)同时解决设施选址和车辆路径问题,使物流系统总成本达到最小,在集成化物流配送网络规划中具有重要意义。针对带仓库容量约束和路径容量约束的选址—路径(CLRP)问题,提出了一种结合模拟退火算法的混合遗传算法进行整体求解。改进混合遗传算法分别对初始种群生成方式、遗传操作和重组策略进行改进,并实现了模拟退火的良好局部搜索能力与遗传算法的全局搜索能力的有效结合。运用一组Barreto Benchmark算例进行数值实验测试其性能,并将求解结果与国外文献中的启发式算法进行比较,验证了改进混合算法的有效性和可行性。     

Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm

This paper proposes a TSK-type recurrent neuro fuzzy system (TRNFS) and hybrid algorithm- GA_BPPSO to develop a direct adaptive control scheme for stable path tracking of mobile robots. The TRNFS is a modified model of the recurrent fuzzy neural network (RFNN) to obtain generalization and fast convergence. The TRNFS is designed using hybridization of genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO), called GA_BPPSO. For the tracking control of mobile robot, two TRNFSs are designed to generate the control inputs by direct adaptive control scheme and hybrid algorithm GA_BPPSO. Through simulation results, we demonstrate the effectiveness of our proposed controller.     

Nonlinear system identification using optimized dynamic neural network

In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme—Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.     

Synchronization of general chaotic systems using neural controllers with application to secure communication

The main contribution of this paper is to propose a nonlinear robust controller to synchronize general chaotic systems, such that the controller does not need the information of the chaotic system’s model. Following this purpose, in this paper, two methods are proposed to synchronize general forms of chaotic systems with application in secure communication. The first method uses radial basis function neural network (RBFNN) as a controller. All the parameters of the RBFNN are derived and optimized via particle swarm optimization (PSO) algorithm and genetic algorithm (GA). In order to increase the robustness of the controller, in the second method, an integral term is added to the RBF neural network gives an integral RBFNN (IRBFNN). The coefficients of the integral term and the parameters of IRBFNN are also derived and optimized via PSO and GA. The proposed methods are applied to the famous Lorenz chaotic system for secure communication. The performance and control effort of the proposed methods are compared with the recently proposed PID controller optimized via GA. Simulation results show the superiority of the proposed methods in comparison to the recent one in improving synchronization while using smaller control effort.