基于神经网络及遗传算法的电渣熔铸优化系统设计

电渣熔铸是一种将精炼和成形相结合的技术,在实际生产中既要充分发挥它在材料提纯精炼方面的优势,又要兼顾生产效率和生产成本等因素,因此是一个典型的多目标组合优化的问题。为此,提出了用电极锥头提纯系数、自耗电极熔化率和有效功率因数3个指标作为最终优化目标,以熔铸电流、渣池深度和冷却水流量为设计变量,基于人工神经网络和遗传算法理论,在Matlab平台上建立了电渣熔铸工艺参数的多目标优化系统,较好地协调了3个指标的相互关系,并预测了最佳的工艺参数组合。     

Combining Neural Network Forecasts on Wavelet-transformed Time Series

We discuss a simple strategy aimed at improving neural network prediction accuracy, based on the combination of predictions at varying resolution levels of the domain under investigation (here: time series). First, a wavelet transform is used to decompose the time series into varying scales of temporal resolution. The latter provides a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. Then, a dynamical recurrent neural netork is trained on each resolution scale with the temporal-recurrent backpropagation algorithm. By virtue of its internal dynamic, this general class of dynamic connections network approximates the underlying law governing each resolution level by a system of non-linear difference equations. The individual wavelet scale forecasts are afterwards recombined to form the current estimate. The predictive ability of this strategy is assessed with the sunspot series.     

基于神经网络的镍基高温合金蠕变断裂寿命研究

利用人工神经网络，使用BP算法对镍基高温合金蠕变断裂寿命进行研究。建立不同成分镍基变形合金在不同温度下，外应力与蠕变断裂寿命之间关系模型，并进行网络训练，对合金的蠕变断裂寿命进行模拟。结果表明，模拟结果与实测结果符合良好，采用人工神经网络方法可以为镍基变形合金蠕变断裂寿命的预测提供一种有效的手段。     

基于规则的人工神经网络FMS动态调度

本文在确定FMS动态调度的优化目标和策略的前提下，提出了基于规则的反向传播学习算法（BP）的神经网络FMS动态调度的方法，FMS的生产状态参数作为神经网络的输入，动态调度策略作为网络的输出，通过实例验证本文提出FMS动态调度的方法简单性，灵活性和实时性等特点。     

Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction

This paper describes a medical application of modular neural networks (NNs) for temporal pattern recognition. In order to increase the reliability of prognostic indices for patients living with the acquired immunodeficiency syndrome (AIDS), survival prediction was performed in a system composed of modular NNS that classified cases according to death in a certain year of follow-up. The output of each NN module corresponded to the probability of survival in a given year. Inputs were the values of demographic, clinical and laboratory variables. The results of the modules were combined to produce survival curves for individuals. The NNs were trained by backpropagation and the results were evaluated in test sets of previously unseen cases. We showed that, for certain combinations of NN modules, the performance of the prognostic index, measured by the area under the receiver operating characteristic curve, was significantly improved (p 0.05). We also used calibration measurements to quantify the benefits of combining NN modules, and show why, when and how NNs should be combined for building prognostic models.     

基于遗传-神经网络数控系统插补控制技术

将遗传算法和神经网络两种技术相结合,建立遗传-神经网络插补模型以实现对复杂型面零件插补,该模型兼具神经非线性映射能力和遗传算法快速、收敛学习能力等性能。通过实验分析,验证了遗传-神经网络数控插补的可行性,该方法能够提高复杂型面零件插补的精度及速度。     

基于神经网络状态观测器的反演控制在双关节机械手控制中的应用

提出了一种基于改进的BP神经网络的自适应状态观测器,该类观测器无需系统的精确模型即可得到收敛于真实状态的状态观测值。利用Lyapunov直接法分析了基于状态输出误差的状态观测器的稳定性。然后,将状态观测器与反演控制器分开设计,以实现观测器得到的速度估计值代替实际速度,避免了实际应用中对速度信号的测量。最后通过对二关节机械手系统的仿真与比较,说明该控制方法的有效性。     

Combining Boolean Neural Architectures for Image Recognition

This paper presents a completely integrated Boolean neural architecture, where a selforganizing Boolean neural network (SOFT) is used as a front-end processor to a feedforward Boolean network based on goal-seeking principles (GSN f). This paper will evaluate the advantages of the integrated SOFT-GSN f over GSN f by showing its increased effectiveness in an optical character recognition task.     

A Neural Gripper for Arbitrary Object Grasping

This paper presents a two-stage neural system to determine the contact points between a three-fingered gripper and an object of arbitrary shape. In the first stage, a CCD camera captures the image of the object and such an image is transformed into a two-dimensional outline through a nearest neighbour algorithm. In the second phase, two neural networks, functioning in cascade, select three contact points in the outline. A competitive Hopfield neural network defines an approximate polygon considering a reduced number of boundary points of the original outline. Then, a supervised neural network, either a multi-layer perceptron or a radial basis function (RBF) network, find the contact points. The experiments suggest that the RBF network trained by the global ridge regression method is suitable for on-line applications and presents the best overall performance in terms of accuracy and robustness to noise. Moreover, this method is able to find correctly the contact points for objects of arbitrary shapes.     

基于人工神经网络研究原油腐蚀的影响因素

针对原油对储运设备腐蚀影响的复杂性,本工作借助人工神经网络输入节点的筛选规则,对影响原油腐蚀性的主要因素进行了筛选,影响因素从最初的18个筛选到最后的9个;然后分别以18个和9个因素作为输入节点构建神经网络模型,通过对比两个模型的预测精度发现,9个输入因素的神经网络模型预测精度更高.对单一影响因素进行敏感性分析,研究了...     

波信号的解调和人工神经网络的损伤识别算法(英文)

讨论了基于波信号的解调和人工神经网络的损伤识别算法,以及其在Lamb波信号的应用。Lamb波与损伤相互作用,将修改回波信号,从该信息提取相关的损害信息可用于自动损伤检测。然而,由于该波与损害相互作用的复杂性,波信号的反应是不容易解释。反应的波信号被认为是一个高频率载波信号调制的低频信号。基线减法后,频域卷积和滤波,原来的信号解调成一个新的简单的信号,其与因损伤发生的能量变化有关。随后进行特征提取,通过寻找新信号的局部极大值和所取得的峰值和位置将作为人工神经网络的损伤特性的输入。这种损伤检测验证算法的有效性,通过一个带缺口复合材料层压板缺损模型利用有限元进行验证。对不同缺口深度和位置的反应波信号用于模拟和训练和测试的样本。最后,对网络的精度和泛化能力进行评估,结果是令人满意的。     

基于神经网络和遗传算法的工业机器人不均匀表面抛光

传统抛光过程中,抛光参数通常是根据工件表面设置为恒定值。但如果工件表面不均匀,恒定的抛光参数对于材料去除量大的区域会发生欠抛光现象,进而降低抛光效率,影响加工表面质量。为此,基于神经网络(NNW)和遗传算法(GA)提出一种工业机器人不均匀工件表面抛光算法,解决不均匀表面抛光过程中出现的问题。应用神经网络预测某一确定的抛光参数对应的抛光性能,利用训练的神经网络模型输出包括最佳材料去除率和改善表面粗糙度的目标函数;将遗传算法用于优化模型抛光参数。通过对不均匀表面的抛光实验,验证了该算法的有效性。     

基于CFD仿真与BP神经网络的离心泵叶轮优化设计

为提高离心泵叶轮的效率,提出了神经网络与遗传算法相结合的设计方法。通过均匀试验设计与CFD仿真获得样本数据点集,进而运用BP神经网络技术建立离心泵的效率与影响因素之间的代理模型。最后,利用遗传算法求取该优化模型,即可得到所求问题的最优解。将这一方法用于某离心泵叶轮的优化,结果表明该方法可以获得很好的效果。     

Feedforward Neural Network;Construction Algorithm;Maximum Likelihood Function;Quasi-newton Methods.

A new method for constructing a feedforward neural network is proposed. The method starts with a single hidden unit and more units are added to the hidden layer one at a time until a network that completely recognizes all its input patterns is constructed. The novel idea about this method is that the network is trained to maximize a certain likelihood function and not to minimize the more widely used mean squared error function. We show that when a new hidden unit is added to the network, this likelihood function is guaranteed to increase and this increase ensures the finite termination of the method. We also provide a wide range of numerical results. The method was tested on the n -bit parity problems and the spiral problem. It was able to construct networks having less than n hidden units that solve the n -bit parity problems for n = 4, 5, 6, 7 and 8. The method was also tested on some real-world data and the networks it constructed were shown to be able to predict patterns not in the training set with more than 95% accuracy.     

The Emergence of a 'Language' in an Evolving Population of Neural Networks

The evolution of language implies the parallel evolution of an ability to respond appropriately to signals (language understanding) and an ability to produce the appropriate signals in the appropriate circumstances (language production). When linguistic signals are produced to inform other individuals, individuals that respond appropriately to these signals may increase their reproductive chances but it is less clear what the reproductive advantage is for the language producers. We present simulations in which populations of neural networks living in an environment evolve a simple language with an informative function. Signals are produced to help other individuals categorize edible and poisonous mushrooms, in order to decide whether to approach or avoid encountered mushrooms. Language production, while not under direct evolutionary pressure, evolves as a byproduct of the independently evolving perceptual ability to categorize mushrooms.     

基于BP_GA算法的铸造有效应力预测与优化

为控制铸件凝固过程中的有效应力大小,避免热裂发生,利用有限元分析软件ProCAST对ZL205A铝合金牵引结构件低压铸造过程进行温度场模拟与有效应力预测,选择浇注温度、模具预热温度、传热系数和模具壁厚等影响铸造应力的因素作为设计参数。结合有效应力预测结果,构建4-7-1-1型神经网络和遗传算法以优化铸造工艺。结果表明,神经网络预测平均相对误差为1.45%,预测精度较高。通过遗传寻优方法,发现了最佳工艺参数组合:浇注温度为688℃,模具预热温度为291℃,模具壁厚为150mm,传热系数为1 284W/(m^2·K),并进行试验验证,获得品质较好的铸件。     

基于遗传神经网络的压铸机座板结构优化设计

在对压铸机合模机构进行结构设计时，利用神经网络的非线性映射能力，通过少量样本的有限元分析结果，训练出表述结构参数间函数关系的神经网络模型，然后利用遗传算法的全局寻优性找到神经网络模型表述的目标函数的最优结构参数，从而解决结构优化设计的瓶颈和智能问题，利用这种优化设计策略，设计了压铸机合模机构座板，结果表明了该方法的高效性。     

集成神经网络在铝电解故障诊断中的应用

本文提出基于诊断任务分解的并行神经网络——集成神经网络来对铝电解过程中的故障进行诊断。利用小结构的神经网络学习速度快、局部极小点少等优点．把大结构的神经网络通过任务分解然后集成转化为小结构的神经网络，优化了大结构的神经网络的学习性能，降低了误诊率。     

非线性对象快速神经网络辨识方法

本文使用改进的神经网络模型结构与算法来辨识未知非线性系统，具有辨识精度高，速度快的特点。该方法简单有效，为设计非线性对象控制器提供了一条思路，从而摆脱了用线性模型近似被控对象的粗略做法。算法中，学习率采用随误差变化率而改变的做法减小了学习率选取的盲目性，加速了网络训练过程。     

一种新型径向基函数神经网络在模型辨识方面的应用

采用改进的径向基函数（MRBF）神经网络，利用实测到的发动机飞行试验数据作为学习样本，建立了发动机的辨识模型，并利用这种方法对不同飞行高度发动机的参数进行了辨识。研究结果表明：这种方法具有训练时间短、学习速度快、辨识精度高、实时性好等优点，并可用于在线辨识。