一种改进PSO优化RBF神经网络的新方法

为了克服神经网络模型结构和参数难以设置的缺点,提出了一种改进粒子群优化的径向基函数(RBF)神经网络的新方法.首先将最近邻聚类用于RBF神经网络隐层中心向量的确定,同时对引入适应度值择优选取的原则对基本粒子群算法进行改进,采用改进粒子群(IMPSO)算法对最近邻聚类的聚类半径进行优化,合理的确定了RBF神经网络的隐层结构.将改进PSO优化的RBF神经网络应用于非线性函数逼近和混沌时间序列预测,经实验仿真验证.与基本粒子群(PSO)算法,收缩因子粒子群(CFA PSO)算法优化的RBF神经网络相比较,其在识别精度和收敛速度上都有了显著的提高.     

基于粒子群算法的RBF网络参数优化算法

针对神经网络的一些缺陷,研究神经网络基于粒子群优化的学习算法,将粒子群优化算法用于RBF神经网络的学习训练。提出了一种基于粒子群优化（PSO）算法的径向基（RBF）网络参数优化算法,首先利用减聚类算法确定网络径向基函数中心的个数,再用PSO算法优化径向基函数的中心及宽度,最后用PSO算法训练隐含层到输出层的网络权值,找到神经网络权值的最优解,以达到优化神经网络学习的目的。最后,通过一个实验与最小二乘法优化的神经网络进行了比较,验证了算法的有效性。     

一种基于代数算法的RBF神经网络优化方法

提出了一种新的RBF神经网络的训练方法,采用动态K-均值方法对RBF 神经网络的隐层中心值和宽度进行了优化,用代数算法训练隐层和输出层之间的权值。在对非线性函数进行逼近的仿真中,验证了该算法的有效性。     

激活函数可调的RBF神经网络模型

RBF神经网络是一种新颖的前馈神经网络,相比传统的BP神经网络具有更强的函数逼近、分类能力,以及更快的学习速度.在实际应用和科学研究中,不同径向基函数的选择对RBF神经网络的性能有很大的影响,一般是根据经验或者实验效果来选择适当的径向基函数.本文提出一种激活函数可调的RBF神经网络模型,将具有不同表达能力的径向基函数通过线性组合的形式形成新的神经元激活函数,进而提高神经网络的逼近精度和泛化能力,对于有监督学习,给出了相应的各参数的训练算法和应用举例.     

RBF控制算法在双酚A生产过程中的应用研究

研究化工原料合成流量控制精度问题,由丙酮和母液合成得到的双酚A是一种重要的化工原料.在双酚A生产过程中,为了节省原料、提高产品质量,必须对丙酮和母液流量进行精确控制.针对流量的精确测量问题,为了提高其控制精度,利用RBF神经网络非线性逼近能力,通过遗传算法优化神经网络隐含层的数目与径向基函数的分布密度,提出了一种遗传算法的RBF神经网络流量控制,并进行仿真.仿真实验结果表明,与单纯RBF神经网络算法相比,新算法能避免人为设置神经网络参数的不足,优化RBF网络拓扑结构,有效提高流量控制精度,符合工程应用要求.     

一种基于遗传算法的RBF神经网络优化方法

提出了一种新的RBF神经网络的训练方法，采用遗传算法对RBF神经网络的隐层中心值和宽度进行了优化，用递推最小二乘法训练隐层和输出层之间的权值。在对非线性函数进行逼近的仿真中，验证了该算法的有效性。     

改进粒子群优化Takagi-Sugeno模糊径向基函数神经网络的非线性系统建模

针对复杂非线性系统建模的难点问题,提出了一种基于改进的粒子群优化算法（PSO)优化的T-S模糊径向基函数（RBF）神经网络的新型系统建模算法。该算法将T-S模糊模型良好的可解释性及RBF神经网络的自学习能力相结合,构成T-S模糊RBF神经网络用于系统建模,并采用动态调整惯性权重的改进的PSO算法结合递推最小二乘算法实现网络参数的优化调整。首先,利用所提算法进行了非线性多维函数的逼近仿真,仿真结果均方差（MSE）为0.00017,绝对值误差不大于0.04,逼近精度较高;又将该算法用于建立动态流量软测量模型,并进行了相关的实验研究,动态流量测量结果平均绝对误差小于0.15L/min,相对误差为1.97%,基本满足测量要求,并优于已有算法。上述仿真及实验研究结果表明,所提算法对于复杂非线性系统具有较高的建模精度和良好的自适应性。     

自构造RBF神经网络及其参数优化

径向基函数神经网络的构造需要确定每个RBF的中心、宽度和数目。该文利用改进的聚类算法自动构造RBFN,考虑样本的类别属性,根据样本分布自动计算RBF的中心和宽度,并确定RBF的数目。所有的网络参数采用非线性优化算法来优化。通过IRIS分类问题和混沌时间序列预测评价自构建RBFN的性能,验证参数优化效果。结果表明,自构造RBFN不但能够自动确定网络结构,而且具有良好的模式分类和函数逼近能力。通过对网络参数的非线性优化,该算法明显改善了网络性能。     

一种应用ARPSO优化RBF神经网络的方法

针对径向基函数神经网络参数难以设置以及因此而导致的网络隐层结构不明朗的问题,提出了一种应用控制种群多样性的微粒群（ ARPSO）优化径向基函数神经网络（ RBF）的方法。通过引入“吸引”和“扩散”因子对基本微粒群算法进行改进,并将改进的微粒群算法用于RBF聚类半径的优化,进而能够合理地确定RBF的隐层结构。将用ARPSO优化的RBF神经网络应用于非线性函数逼近,经实验仿真验证,与基本微粒群（ PSO）算法、收缩因子微粒群（ CFA PSO）算法优化的RBF神经网络相比较,在收敛速度和识别精度上有了显著的提高。     

多粒子群优化算法和RBF神经网络在缺陷故障参数红外智能识别中的应用

讨论了利用多粒子群优化算法(Multi-PSO)和径向基函数(RBF)神经网络进行缺陷参数红外识别的途径.PSO算法可以不用计算梯度,算法通用,而使用RBF神经网络作为代理模型,极大简化了复杂、费时的有限元计算,其中训练RBF神经网络的样本由有限元软件的计算结果产生.提出的多粒子群优化算法将粒子群分为若干子群,并利用粒子本身、粒子所在子群以及全局的最优解来更新粒子的速度与位置,该方法收敛速度较慢,但有可能找到问题的多个极小值.最后给出了该方法在缺陷参数红外识别中一个简单的应用例子.     

Multiprocessor scheduling and neural network training methods using shuffled frog-leaping algorithm

In this paper, we designed novel methods for Neural Network (NN) and Radial Basis function Neural Networks (RBFNN) training using Shuffled Frog-Leaping Algorithm (SFLA). This paper basically deals with the problem of multi-processor scheduling in a grid environment. We, in this paper, introduce three novel approaches for the task scheduling problem using a recently proposed Shuffled Frog-Leaping Algorithm (SFLA). In a first attempt, the scheduling problem is structured as a problem of optimization and solved by SFLA. Next, this paper makes use of SFLA trained Artificial Neural Network (ANN) and Radial Basis function Neural Networks (RBFNN) for the problem of task scheduling. Interestingly, the proposed methods yield better performance than contemporary algorithms as evidenced by simulation results.     

An improved Shuffled Frog-leaping Algorithm to optimize component pick-and-place sequencing optimization problem

The component pick-and-place sequence is one of the key factors to affect the working efficiency of the surface mounting machine in the printed circuit board assembly. In this paper, an improved Shuffled Frog-leaping Algorithm was presented by improving the basic Shuffled Frog-leaping Algorithm (SFLA) with the strategy of letting all frogs taking part in memetic evolution and adding the self-variation behavior to the frog. The objective function of component pick-and-place sequence of the gantry multi-head component surface mounting machine was established. Parameters selection is critical for SFLA. In this study, Three-way ANOVA was used in parameters analyzing of the new improved SFLA. The parameters like memeplex numbers m, the frogs’ number P and local evolution numbers i_Part were found having notable effects on the mounting time (time spent for components picking and placing), but the interactions among these parameters were not obvious. Multiple comparison procedures were adopted to determine the best parameter settings. In order to test the performance of the new algorithm, several experiments were carried out to compare the performance of improved SFLA with the basic SFLA and the genetic algorithm (GA) in solving the component pick-and-place sequence optimization problems. The experiment results indicate that improved SFLA can solve the optimization problem efficiently and outperforms SFLA and GA in terms of convergence accuracy, although more CPU time is undeniably needed.     

基于MDL的RBF神经网络结构和参数的学习

本文提出了一种优化径向基函数神经网络（ＲＢＦＮＮ）结构的参数的方法,该方法包括两个过程：训练和进化．训练用梯度下降法学习ＲＢＦＮＮ的中心,宽度和输出权值;进化采用二进制编码的遗传算法（ＧＡ）学习ＲＢＦＮＮ的结构,适应度函数是基于信息论中最小描述长度（ＭＤＬ）原理的目标函数．函数逼近仿真实验证明了该方法比其他方法鲁棒性强,所得到的网络结构简单．     

基于混合递阶遗传算法的径向基神经网络学习算法及其应用

在研究径向基神经网络学习算法的基础上, 提出了一种新型的径向基神经网络学习算法———混合递阶遗传算法. 该算法将递阶遗传算法和最小二乘法的优点结合在一起, 能够同时确定径向基神经网络的结构和参数, 并具有较高的学习效率. 采用基于混合递阶遗传算法的径向基神经网络对混沌时间序列学习和预测, 取得了较好的效果.     

Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm

A novel method based on rough sets (RS) and the affinity propagation (AP) clustering algorithm is developed to optimize a radial basis function neural network (RBFNN). First, attribute reduction (AR) based on RS theory, as a preprocessor of RBFNN, is presented to eliminate noise and redundant attributes of datasets while determining the number of neurons in the input layer of RBFNN. Second, an AP clustering algorithm is proposed to search for the centers and their widths without a priori knowledge about the number of clusters. These parameters are transferred to the RBF units of RBFNN as the centers and widths of the RBF function. Then the weights connecting the hidden layer and output layer are evaluated and adjusted using the least square method (LSM) according to the output of the RBF units and desired output. Experimental results show that the proposed method has a more powerful generalization capability than conventional methods for an RBFNN.     

A novel self-adaptive extreme learning machine based on affinity propagation for radial basis function neural network

In this paper, a novel self-adaptive extreme learning machine (ELM) based on affinity propagation (AP) is proposed to optimize the radial basis function neural network (RBFNN). As is well known, the parameters of original ELM which developed by G.-B. Huang are randomly determined. However, that cannot objectively obtain a set of optimal parameters of RBFNN trained by ELM algorithm for different realistic datasets. The AP algorithm can automatically produce a set of clustering centers for the different datasets. According to the results of AP, we can, respectively, get the cluster number and the radius value of each cluster. In that case, the above cluster number and radius value can be used to initialize the number and widths of hidden layer neurons in RBFNN and that is also the parameters of coefficient matrix H of ELM. This may successfully avoid the subjectivity prior knowledge and randomness of training RBFNN. Experimental results show that the method proposed in this thesis has a more powerful generalization capability than conventional ELM for an RBFNN.     

基于三角函数搜索因子的混合蛙跳算法

针对传统混合蛙跳算法存在收敛速度慢、容易陷入局部最优和搜索精度不高的缺陷,提出了基于三角函数搜索因子的混合蛙跳算法。该算法将基于三角函数搜索因子的局部进化策略和产生新个体策略引入到混合蛙跳算法中,改进混合蛙跳算法的局部搜索精度和全局收敛性能。实验结果表明,基于三角函数搜索因子的混合蛙跳算法能够显著改善混合蛙跳算法的寻优精度和收敛速度,使算法的搜索效率和稳定性同时得到提高。     

The improved radial basis function neural network and its application

Compared with other feed-forward neural networks, radial basis function neural networks (RBFNN) have many advantages which make them more suitable for nonlinear system modeling, and they have recently received considerable attention. In this paper, a RBFNN is employed to model strongly nonlinear systems. First, the problems of nonlinear system modeling are analyzed, and then the structure of the RBFNN as well as the training algorithm are improved to solve these problems. Finally, an industrial high-purity distillation column, which is a strongly nonlinear system, is successfully modeled with the improved RBFNN. Owing to the complexities of a nonlinear system, it is necessary to use a real-time model correction method to modify the parameters of the RBFNN model in real time. One efficient method is proposed in this paper. The idea is to employ the Givens transformation to modify the parameters of the RBFNN-based model. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

A fuzzy hybrid learning algorithm for radial basis function neural network with application in human face recognition

This paper presents a fuzzy hybrid learning algorithm (FHLA) for the radial basis function neural network (RBFNN). The method determines the number of hidden neurons in the RBFNN structure by using cluster validity indices with majority rule while the characteristics of the hidden neurons are initialized based on advanced fuzzy clustering. The FHLA combines the gradient method and the linear least-squared method for adjusting the RBF parameters and the neural network connection weights. The RBFNN with the proposed FHLA is used as a classifier in a face recognition system. The inputs to the RBFNN are the feature vectors obtained by combining shape information and principal component analysis. The designed RBFNN with the proposed FHLA, while providing a faster convergence in the training phase, requires a hidden layer with fewer neurons and less sensitivity to the training and testing patterns. The efficiency of the proposed method is demonstrated on the ORL and Yale face databases, and comparison with other algorithms indicates that the FHLA yields excellent recognition rate in human face recognition.     

求解非线性方程组的蛙跳和 BFGS 混合算法

混合蛙跳算法具有算法简单、控制参数少、易于实现等优点,但缺乏良好的局部细化搜索能力,使得求解精度不高。借鉴BFGS算法强的局部搜索能力,将BFGS算法与混合蛙跳算法有机融合,形成性能更优的混合优化算法,并用来求解非线性方程组。通过3个非线性方程组的实验表明,该混合算法收敛精度较高,收敛速度较快,是一种较好的求解非线性方程组的方法。