基于量子粒子群优化算法的新型正交基神经网络分数阶混沌时间序列单步预测

针对分数阶混沌时间序列预测精度低、速度慢的问题,提出了基于量子粒子群优化(QPSO)算法的新型正交基神经网络预测模型。首先,在Laguerre正交基函数的基础上提出一种新型正交基函数,并结合神经网络拓扑构成新型正交基神经网络;其次,利用QPSO算法优化新型正交基神经网络参数,将参数优化问题转化为多维空间上的函数优化问题;最后,根据已优化参数建立预测模型并进行预测分析。分别以分数阶Birkhoff-shaw和Jerk混沌系统为模型,利用Adams-Bashforth-Moulton预估-校正法产生混沌时间序列作为仿真对象,进行单步预测对比实验。仿真表明,与反向传播(BP)神经网络、径向基函数(RBF)神经网络及普通的新型正交基神经网络相比,基于QPSO算法的新型正交基神经网络的平均绝对值误差(MAE)、均方根误差(RMSE)明显减小,决定度系数(CD)更接近于1,平均建模时间(MMT)明显缩短。实验结果表明,基于QPSO算法的新型正交基神经网络提高了分数阶混沌时间序列预测的精度和速度,便于该预测模型的应用和推广。     

支持向量机和正交设计方法的比较和应用研究

论文对支持向量机(SVM)和正交设计方法进行了比较。支持向量机在分类和回归方面广为应用，而正交设计方法在实验设计方面是非常有效的，并且在化学工业中应用广泛。本文使用了两因素、七维正交实验(干燥实验)作为例子来把支持向量机方法应用到实验设计中去。正交表是用来研究实验的最优化条件和显著因素，本文给出了支持向量机和正交实验设计的计算结果。通过两者的比较，可以看到支持向量在实验预测方面比正交设计方法效果更好。从而可知支持向量机在实验设计方面的前景广阔。     

基于复合正交神经网络的自适应逆控制系统

目前，在自适应逆控制系统中常采用BP神经网络，而BP网络存在算法复杂、易陷入局部极小解等不足。而正交神经网络能克服BP网络的不足，但由于正交神经网络学习算法存在某些局限性，提出了一种复合正交神经网络，该正交网络结构与三层前向正交网络相同，不同的是正交网络的隐单元处理函数采用带参数的Sigmoid函数的复合正交函数，该神经网络算法简单，学习收敛速度快，并能对网络的函数参数进行优化，为非线性系统的动态建模提供了一种方法。仿真实验表明，网络在用于过程的自适应逆控制中具有很高的控制精度和自适应学习能力。该动态神经网络比其它神经网络具有更强的建模能力与学习适应性，有线性、非线性逼近精度高等优异特性，非常适合于实时控制系统。     

基于增强径向函数神经网络的错误定位方法

结合径向基函数神经网络与正交实验设计理论,提出了一种增强径向基函数神经网络错误定位算法.根据选择的测试用例执行得到源程序的语句覆盖信息和执行结果;通过神经网络计算出每条语句的可疑度值,并通过正交实验设计方法自适应调整神经网络中的参数值;最后按照可疑度值由高到低的顺序逐条检查程序的可疑语句进行错误定位.通过实验对所提出方法与径向基函数神经网络算法以及反向传播神经网络算法进行比较分析,结果表明,基于增强径向基函数神经网络算法具有更精确的错误定位效果和更显著的定位效率.     

一种基于演化算法的BP改进算法

本文在对BP神经网络算法分析的基础上,提出一种基于演化算法的BP改进算法(EBP)。该算法将演化算法运用到BP算法学习率的求解中,从而达到学习率的自适应、自组织的目的。实验结果表明,使用EBP算法进行求解函数逼近、优化和建模等BP神经网络应用问题,都要比传统的BP算法具有更好的精确度和收敛速度,并且能够克服传统BP算法易陷入局部最优解、学习过程出现震荡等缺点。     

基于正交小波变换分数间隔的神经网络盲均衡算法

针对分数间隔盲均衡算法(T/4-FSE-CMA)收敛速度慢、稳态误差大的缺点,提出了一种基于正交小波变换分数间隔的神经网络盲均衡算法(T/4-FSE-WT-FNN).该算法采用四路子信道模型,以神经网络作为均衡器,同时,对均衡器的输入信号做正交小波变换并进行归一化,与基于正交小波变换的前馈神经网络盲均衡算法(WT-FN...     

机械臂的CONN控制与CMAC控制的比较研究

由于正交神经网络算法简单，学习收敛速度快，具有线性、非线性逼近精度高等优异特性，取得了较好的应用效果，但在机器人动态建模与实时控制问题上研究较少。为此在机械臂的神经网络控制中，该文提出复合正交神经网络(CONN)与PID并行控制方法，并对小脑模型(CMAC)与PID并行控制作一比较研究。仿真结果表明，当阶跃输入与正弦输入时CONN与CMAC实现的前馈控制具有相同的控制效果，但CONN算法比CMAC算法更简单，这充分地体现了复合正交神经网络的特点。     

基于正交实验设计的语音识别特征参数优化

鉴于语音识别性能与所选用的语音特征参数密切相关,提出一种系统性的实用的特征参数优化方法——基于方差的正交实验设计法。首先进行因素(语音特征参数)和水平的选择,再根据数理统计与正交性原理,从大量的实验点中挑选适量的具有代表性、典型性的点构造正交表进行正交实验,最后通过计算对正交实验结果进行分析,找出最优的特征参数组合。与目前参数的简单组合方案相比较,新方法的误识率下降了5.6%,响应时间减少了181.37ms。实验结果表明,正交实验设计用于语音特征参数优化是有效的,对后续研究具有指导意义。     

基于ACP方法的载人登月中止规划的计算实验研究

针对载人登月中止规划存在的不确定性因素, 提出了基于ACP (Artificial systems, computational experiments, parallel execution)方法的载人登月中止规划框架, 论述了该框架下人工系统和平行执行的初步设计, 主要讨论了计算实验设计、分析和验证过程. 针对中止规划时中止点状态误差的不确定性, 提出利用短时间的累积观测值确定中止点状态误差的计算实验方法, 并应用模拟退火单纯形混合算法求解从载人登月轨道上任一点返回地球的中止机动方案. 最后给出基于正交实验设计的计算实验示例性算例, 验证本文提出方法的有效性.     

BP神经网络学习算法的改进与应用

本文利用BP神经网络拟合电化领域的工艺模型，并通过正交实验获得实验样本数据，对BP网络进行演化训练。在实践应用中，对BP网络的演化学习进行了改进，取得了良好的效果。实践证明，用该模型得到的最优工艺流程的预期值误差和稳定性，都能满足实际生产的要求。     

神经网络用于合成条件的预测及优化

以正交试验的结果，完成了神经网络的学习，以十二醇琥珀酸单酯磺酸钠合成条件为例，预测了醇酐投料比，酯化温度和酯化时间对产物得率的影响，预测结果与试验结果比较相对误差不大于１．０％。     

Chebyshev神经网络辨识器

基于逼近论，将一组Ｃｈｅｂｙｓｈｅｖ正交多项式取代ＢＰ网络中的Ｓ型函数，构成一种新的神经网络模型。理论分析和仿真实验表明，该网络可逼近任意非线性系统，且建模容易，收敛速度快，学习次数远远少于ＢＰ网络。     

A new synthesis approach for feedback neural networks based on theperceptron training algorithm

In this paper, a new synthesis approach is developed for associative memories based on the perceptron training algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron training is evident. The perceptron training in the synthesis algorithms is guaranteed to converge for the design of neural networks without any constraints on the connection matrix. For neural networks with constraints on the diagonal elements of the connection matrix, results concerning the properties of such networks and concerning the existence of such a network design are established. For neural networks with sparsity and/or symmetry constraints on the connection matrix, design algorithms are presented. Applications of the present synthesis approach to the design of associative memories realized by means of other feedback neural network models are studied. To demonstrate the applicability of the present results and to compare the present synthesis approach with existing design methods, specific examples are considered.     

Analysis and synthesis of a class of discrete-time neural networksdescribed on hypercubes

A qualitative analysis is presented for a class of synchronous discrete-time neural networks defined on hypercubes in the state space. Analysis results are utilized to establish a design procedure for associative memories to be implemented on the present class of neural networks. To demonstrate the storage ability and flexibility of the synthesis procedure, several specific examples are considered. The design procedure has essentially the same desirable features as the results of J. Li et al. (1988, 1989) for continuous-time neural networks. For a given system dimension, networks designed by the present method may have the ability to store more patterns (as asymptotically stable equilibria) than corresponding discrete-time networks designed by other techniques. The design method guarantees the storage of all the desired patterns as asymptotically stable equilibrium points. The present method provides guidelines for reducing the number of spurious states and for estimating the extent of the patterns' domains of attraction. The present results provide a means of implementing neural networks by serial processors and special digital hardware.     

Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays

This work presents a study on the applicability of radial base function (RBF) neural networks for prediction of Roughness Average (R_a) in the turning process of SAE 52100 hardened steel, with the use of Taguchi’s orthogonal arrays as a tool to design parameters of the network. Experiments were conducted with training sets of different sizes to make possible to compare the performance of the best network obtained from each experiment. The following design factors were considered: (i) number of radial units, (ii) algorithm for selection of radial centers and (iii) algorithm for selection of the spread factor of the radial function. Artificial neural networks (ANN) models obtained proved capable to predict surface roughness in accurate, precise and affordable way. Results pointed significant factors for network design have significant influence on network performance for the task proposed. The work concludes that the design of experiments (DOE) methodology constitutes a better approach to the design of RBF networks for roughness prediction than the most common trial and error approach.     

Nonlinear modeling and adaptive fuzzy control of MCFC stack

To improve availability and performance of fuel cells, the operating temperature of molten carbonate fuel cells (MCFC) stack should be controlled within a specified range. However, the most existing models of MCFC are not ready to be applied in synthesis. In this paper, a radial basis function neural networks identification model of MCFC stack is developed based on the input–output sampled data. A novel adaptive fuzzy control procedure for the temperature of MCFC stack is also developed. The parameters of the fuzzy control system are regulated by back-propagation algorithm, and the rule database of the fuzzy system is also adaptively adjusted by the nearest-neighbor-clustering algorithm. Finally using the neural networks model of MCFC stack, the simulation results of the control algorithm are presented. The results show the effectiveness of the proposed modeling and design procedures for MCFC stack based on neural networks identification and the novel adaptive fuzzy control.     

Synthesis of Neural Networks and PID Control for Performance Improvement of Industrial Robots

In this article, an approach for improving the performance of industrialrobots using multilayer feedforward neural networks is presented. Thecontroller based on this approach consists of two main components: a PIDcontrol and a neural network. The function of the neural network is tocomplement the PID control for the specific purpose of improving theperformance of the system over time. Analytical and experimental resultsconcerning this synthesis of neural networks and PID control are presented.The analytical results assert that the performance of PID-controlledindustrial robots can be improved through proper utilization of the learningand generalization ability of neural networks. The experimental results,obtained through actual implementation using a commercial industrial robot,demonstrate the effectiveness of such control synthesis for practicalapplications. The results of this work suggest that neural networks could beadded to existing PID-controlled industrial robots for performanceimprovement.     

A comparison of experimental designs in the development of a neural network simulation metamodel

In this case study, we investigate the effects of experimental design on the development of artificial neural networks as simulation metamodels. A simple, deterministic combat model developed within the paradigm of system dynamics provides the underlying simulation. The neural network metamodels are developed using six different experimental design approaches. These include a traditional full factorial design, a random sampling design, a central composite design, a modified Latin Hypercube design and designs supplemented with domain knowledge. The results from this case study show how much impact the experimental design chosen for the neural network training set can have on the predictive accuracy achieved by the metamodel. We compare the networks in terms of various performance measures. The neural network developed from the modified Latin Hypercube design supplemented with domain knowledge produces the best performance, outperforming networks developed from other designs of the same size.     

A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests

In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm.The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis.     

Inverse Back Analysis Based on Evolutionary Neural Networks for Underground Engineering

In essence, back analysis is a process of system identification. Therefore, artificial neural networks represent a suitable solution methodology for this problem. To overcome the shortcomings of the neural networks and evolutionary neural networks, based on immunized evolutionary programming, a new evolutionary neural network whose architecture and connection weights simultaneously evolve is proposed. Using this new evolutionary neural network, a novel inverse back analysis for underground engineering is studied. Using a numerical example and a real engineering example, namely, an underground roadway of the Huainan coal mine in China, the accuracy of this inverse back analysis is verified. Moreover, the non-uniqueness of the solution generated by the inverse back analysis is analyzed. The results show that, using the back-calculated parameters, the computed displacements agree with the measured ones. Thus, the new inverse back analysis method is demonstrated to be a high-performance method for usage in underground engineering. Moreover, various other conclusions can be drawn: the training samples of the neural network should be collected from the results of the positive analysis by the finite element method and selected based on the orthogonal experimental design, and the precision of the back analysis using multiple parameters is worse than that using a single parameter.