基于遗传模拟退火算法的BP算法研究

目前广泛应用于神经网络优化的方法是反向传播(Back Propagation,BP),但是BP神经网络的全局搜索能力很有限.文中探讨了两种全局优化算法:遗传算法(Genetic Algorithm,GA)和模拟退火(Simulated Annealing,SA),以及它们和BP算法结合形成的优化算法,并且比较了它们在神经网络优化中的优缺点.     

Genetic design of real-time neural network controllers

The use of genetic algorithms to design neural networks for real-time control of flows in sewerage networks is discussed. In many control applications, standard supervised learning techniques (such as back-propagation) cannot be used through lack of training data. Reinforcement learning techniques, such as genetic algorithms, are a computationally-expensive but viable alternative if a simulator is available for the system in question. The paper briefly describes why genetic algorithms and neural networks were selected, then reports the results of a feasibility study. This demonstrates that the approach does indeed have merits. The implications of high computational cost are discussed, in terms of scaling up to significantly complex problems.     

一种基于遗传算法的神经网络控制方法

本文比较了传统的BP算法和遗传算法用于神经网络设计的优缺点，阐明了遗传算法和神经网络相结合的必要性，提出了一种用遗传算法同时优化网络的结构和权值的神经网络控制方法，通过对遗传算法基本参数及骗码方案，遗传算子的设计，实现了权值与结构的同时优化，成功地应用于二级倒立摆系统的控制，仿真结果显示了这种遗传算法能够有效抑制早期收敛，以较快的速度与较高的精度达到全局快速收敛。     

基于遗传算法的特征选择方法

特征提取广泛应用于模式识别、知识发现、机器学习等诸多领域,并受到了越来越多的关注犤1犦。对于一个给定的待分类模式,特征提取要求人们从大量的特征中选取一个最优特征子集,以代表被分类的模式。该文对特征提取这一组合优化及多目标优化问题提出了基于遗传算法的解决方法,把遗传算法作为识别或分类系统的“前端”,找出代表问题空间的最优特征子集,以大大降低分类系统的搜索空间,从而提高搜索效率。     

基于遗传算法的图像阈值选取

阈值分割是图像分割中的一种常用的方法,传统的OTSU阈值化方法对信噪比较低的图像分割效果不理想,而二维最大类间方差法充分利用像素与领域的空间相关信息,能够产生较为满意的分割结果,但运算量大大增加,文中采用遗传算法,通过编码、选择、交叉、变异等操作对二维最大类间方差法进行了优化,试验结果表明,遗传算法可以大大提高运算速度,对算法的优化效果十分明显。     

A genetic algorithm-based artificial neural network model for the optimization of machining processes

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.     

遗传算法在排水管网优化中的应用研究

本文以某开发区的合流制排水管网设计为工程实例进行了深入研究,采用了近年来广泛应用于工程界的遗传算法。通过对基本遗传算法和改进的遗传算法在不同运算参数组合方案时的多次模拟实验结果进行比较,提出采用种群为25和最优保存策略时,由自适应交叉率和变异率组合构成的方案能够最迅速地收敛于最优解。     

基于遗传算法的图像阈值的自动选取

在论述了遗传算法基本原理的基础上,以最大类间方差法为例,讨论了如何将遗传算法应用到图像阈值的自动选取之中,并给出了具体的操作步骤。实验结果表明,利用遗传算法可以大大提高原有图像阈值选取方法的性能。     

Stability analysis for stochastic neural network with infinite delay

This paper considers a stochastic neural network (SNN) with infinite delay. Some sufficient conditions for stochastic stability, stochastic asymptotical stability and global stochastic asymptotical stability, respectively, are derived by means of Lyapunov method, Itô formula and some inequalities. As a corollary, we show that if the neural network with infinite delay is stable under some conditions, then the stochastic stability is maintained provided the environmental noises are small. Estimates on the allowable sizes of environmental noises are also given. Finally, a three-dimensional SNN with infinite delay is analyzed and some numerical simulations are illustrated to show our results.     

The scope of artificial neural network metamodels for precision casting process planning

Precision investment casting process planning has been tackled in the past according to experience. Recently, casting simulation software is being increasingly used to predict product quality by implementing ‘what-if’ scenarios. Input parameters include relatively simple factors such as mould temperature, melting temperature, casting material. They also include factors whose influence is more complex to quantify, such number and location of feeding points, diameter and length of inflow channels, angle of channel with respect to the main sprue axis. Simulation results cannot help the engineer for workpieces other than the one simulated. In this paper a series of feedforward artificial neural network (ANN) models is presented aiming at such generalisation. To achieve this, a large number of software simulation runs were conducted for a number of different small parts, with varying runner geometry and casting conditions. The parameters characterising part geometry have been chosen to be surface area and volume-to-area ratio. The different ANN models predictive capabilities are reflected to the respective training and generalisation errors. A user-friendly interface has been conducted for model execution in a complete application, whose main virtue is expandability.     

Improving neural network models of physical systems through dimensional analysis

The paper presents a technique for generating concise neural network models of physical systems. The neural network models are generated through a two-stage process. The first stage uses information embedded in the dimensions or units in which the data is represented. Dimensional analysis techniques are used initially to make this information explicit, and a limited search in the neural network architecture space is then conducted to determine dimensionless representations of variables/parameters that perform well for a given model complexity. The second stage uses information available in the numerical values of the data to search for high-level dimensionless variables/parameters, generated from simple combinations of dimensionless quantities generated in the first stage and which result in concise neural network models with improved performance characteristics. The search for these high-level dimensionless variables/parameters is conducted in an enhanced representation space using functional link networks with flat or near flat architectures. The use and effectiveness of the technique is demonstrated for three applications. The first is the design and analysis of reinforced concrete beams, which is representative of the class of problems associated with the design and analysis of composites. The second is the classical elastica problem, for predicting non-linear post-buckled behaviour of columns and the third, the analysis of a bent bar under a specified combination of loads.     

Classifying inventory using an artificial neural network approach

This paper presents artificial neural networks (ANNs) for ABC classification of stock keeping units (SKUs) in a pharmaceutical company. Two learning methods were utilized in the ANNs, namely back propagation (BP) and genetic algorithms (GA). The reliability of the models was tested by comparing their classification ability with two data sets (a hold-out sample and an external data set). Furthermore, the ANN models were compared with the multiple discriminate analysis (MDA) technique. The results showed that both ANN models had higher predictive accuracy than MDA. The results also indicate that there was no significant difference between the two learning methods used to develop the ANN.     

遗传算法求解函数极值的应用

通过用遗传算法求高等数学中的函数极值问题，说明了遗传算法对连续、可导等条件的放宽，同时，也体现了遗传算法在求解高等数学中函数极值的良好应用。     

一个基于神经网络和遗传算法的游戏自主角色的设计

具有学习能力和动态适应环境变化的自主角色已经成为商业游戏的一个卖点,采用传统的人工智能方法往往无法实现复杂的自主角色的行为,基于此,采用非确定性的神经网络和遗传算法来实现自主角色已经成为当前游戏人工智能的一个热点。分析了游戏自主角色的特点, 建立了NPC的自主认知模型,同时采用神经网络和遗传算法相结合的游戏自主角色的设计思路,利用遗传算法优化神经网络的方法设计了一个自主角色的框架,建立了一个游戏角色的自学习模型,通过仿真实验表明采用神经网络和遗传算法相结合的非确定性算法形成的游戏角色的自学习系统要比传统的NPC角色更加自主和智能化。     

Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.     

遗传算法求解函数极值的应用

通过用遗传算法求高等数学中的函数极值问题,说明了遗传算法对连续、可导等条件的放宽,同时,也体现了遗传算法在求解高等数学中函数极值的良好应用.     

基于遗传算法和神经网络预测的再励学习

提出一种基于遗传算法和神经网络预测法相结合的再励学习方法,利用遗传算法对全局进行最优解搜索,将进化过程中产生的数据用来训练神经网络预测器,当再励学习逼近最优解时,利用预测网络估计动作网络的参数、结构与系统响应之间的映射关系,用预测网络逼近最优解的能力引导遗传算法在局部向最优解快速逼近,以解决遗传算法局部振荡问题,从而实现快速学习的能力。将其应用于矢量控制交流电机的速度环控制器自学习中,仿真实验验证了该算法的有效性。     

基于遗传算法的数字水印优化方法

利用遗传算法搜索出最优的初始值和嵌入强度,提出了一种新的离散多小波变换域的数字图像水印算法.实验结果表明,使用此算法能提高数字水印对于各种改变的鲁棒性,同时保证数字水印的不可感知性.     

最小风险准则和遗传算法优化神经网络

提出了一种基于最小平均风险准则的遗传算法优化设计前向神经网络的方法，遗传算法的适应度函数并不采用传统的均方误差准则，而是由平均风险则所决定，这种方法在神经网络输出与期望输出之间误差的同时，还要考虑神经网络对不同类型训练样本产生的这种误差所引起的不同的风险损失。这种方法优化得到的神经网络不仅可以准确地再现训练样本集合的期望输出，对训练样本集合外样本的正确预测能力也有明显的提高。