基于PSD算法的单神经元PID控制器在汽温控制中的应用

介绍将自适应PSD控制算法中递推计算并修正增益的方法引入单神经元PID控制，形成了具有增益自适应能力的控制器，设计了基于ISD算法的单神经元PID控制器，并应用于超临界机组过热汽温控制系统。仿真结果表明，基于PSD算法的单神经元PID控制器具有较强的自适应能力和鲁棒性，其控制品质优于常规的PID控制器和一般单神经元控制器。     

基于小波神经网络的系统辨识方法

神经网络由于具有良好的自学习和自适应能力，在非线性黑箱建模或系统辨识中有着广泛的应用，这些辨识模型有：多层感知器、径向基函数网和反馈网络等等。文中提出了基于小波神经网络模型的系统辨识方法。由于小波变换或分解所表面的良好的时频局部化特性，以及多尺度的功能，我们用规范正交的小波函数作为基函数网络中的基函数，得到所谓的小波神经网络。通过计算机仿真证实了该方法的良好的辨识效果。     

复合式粉粒物料水分测量的新方法

针对粉粒物料水分在线连续测量存在的问题进行了分析，将快速失重法和电导法有机结合对复合式水分测量方案进行了研究，通过实验得出了复合式水分测量模型，为粉粒物料水分测量提供了新的方法。     

基于LabVIEW变工作点智能控制的实现

设计了基于LabVIEW平台的变工作点温度单神经元自适应PID智能控制器,应用于变粘度油流量试验装置的变工作点变参数的温度控制,试验表明此方法是可行的。     

基于LabVIEW变工作点智能控制的实现

设计了基于LabVIEW平台的变工作点温度单神经元自适应PID智能控制器,应用于变粘度油流量试验装置的变工作点变参数的温度控制,试验表明此方法是可行的.     

基于增量学习思想的改进AdaBoost 建模方法

针对软测量建模的特点以及建模过程中存在的主要问题,提出了基于 AdaBoost RT 集成学习方法的软测量建模方法,并根据 AdaBoost RT 算法固有的不足和软测量模型在线更新所面临的困难,提出了自适应修改阈值 和增添增量学习性能的改进方法.使用该建模方法对宝钢300 t LF 精炼炉建立钢水温度软测量模型,并使用实际生产数据对模型进行了检验.检验结果表明,该模型具有较好的预测精度,能够很好地实现在线更新.     

燃料电池温度模糊自适应控制

如何控制燃料电池温度性能是燃料电池的一个重要问题。首先基于模糊辨识建模方法建立质子交换膜燃料电池温度性能的T-S模型。模型结构简单,精度高,方便地应用于质子交换膜燃料电池系统控制中。其次针对该模型设计电堆温度的模糊自适应控制器。最后在Matlab平台进行仿真,模糊自适应控制器在较大幅度变化的系统参数下都得到较好的控制性能,证明模糊自适应控制系统具有很好的鲁棒性和良好的控制品质,能够满足质子交换膜燃料电池温度控制系统的要求。     

基于自适应模糊聚类的神经网络软测量建模方法

提出一种基于模糊聚类的神经网络软测量建模方法．该方法采用数据分组训练、自动确定模糊分类数、在线测量时分类中心自适应修正，降低了计算量，提高了建模精度．将该算法用于步进式加热炉钢坯温度预报的仿真结果表明，它能够解决钢坯温度难以在线测量的问题。     

一种动态软测量建模方法及其工业应用

针对目前静态软测量建模方法无法反映工业过程动态信息,造成模型预测精度低、鲁棒性差等问题,提出了一种基于模糊曲线和高斯过程的动态软测量建模方法.该方法采用模糊曲线法对输入数据进行处理,并利用处理后的数据构建新的数据集,最后采用高斯过程建立软测量模型.将提出的动态软测量模型应用于PTA氧化过程中4-CBA含量的预测,结果表明,所建模型运算速度快、预测精度高.     

复合式粉粒物料水分测量的新方法

针对粉粒物料水分在线连续测量存在的问题进行了分析，将快速失重法和电导法有机结合，对复合式水分测量方案进行了研究，通过实验得出了复合式水分测量模型．为粉粒物料水分测量提供了新的万法。     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

非线性权函数感知器的一种线性快速学习算法

高阶感知器是神经元状态变量的非线性化，它是一阶感知器的非线性推广，除了神经元状态变量的非线性化推广外，对权向量函数的非线性推广而得到的感知器，文中定义为具有非线性权向量函数的感知器，由于感知器的权重及作用函数都是非性函数，当感知器接近最优点时，其连接权调节幅度很小，采用对非线性权函数及非线性作用函数分别进行Taylor展开，并取其一式近似逼近原函数，从而使其非线性权函数及非线性作用函数都转化为线性函数，简化了感知器学习过程的计算量，加快了感知的学习过程。最后，给出了具有非线性权函数感知器的线性化学习算法。     

Pattern classification by multi-layer perceptron using fuzzy integral-based activation function

A multi-layer perceptron with single output node can be served as a classifier for two-class problems. Traditionally, an activation function such as the sigmoid function of a neuron performs the linear multi-regression model, which assumes that there is no interaction among attributes. However, because the interaction should not be ignored, this paper uses a non-linear fuzzy integral to replace the linear form by interpreting the connection weights as the values of the fuzzy measure and the degrees of importance of the respective input signals for the fuzzy integral-based sigmoid function. A fitness function of maximizing the number of correctly classified training patterns and minimizing the errors between the actual and desired outputs of individual training patterns is incorporated into the genetic algorithm to obtain appropriate parameter specifications. The experimental results further demonstrate that the perceptron with the fuzzy integral-based sigmoid function performs well in comparison with the traditional multi-layer perceptron.     

A general insight into the effect of neuron structure on classification

This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic activation functions, and the result is compared with two well-known monotonic activation functions.     

On the classification capability of sign-constrained perceptrons

The perceptron (also referred to as McCulloch-Pitts neuron, or linear threshold gate) is commonly used as a simplified model for the discrimination and learning capability of a biological neuron. Criteria that tell us when a perceptron can implement (or learn to implement) all possible dichotomies over a given set of input patterns are well known, but only for the idealized case, where one assumes that the sign of a synaptic weight can be switched during learning. We present in this letter an analysis of the classification capability of the biologically more realistic model of a sign-constrained perceptron, where the signs of synaptic weights remain fixed during learning (which is the case for most types of biological synapses). In particular, the VC-dimension of sign-constrained perceptrons is determined, and a necessary and sufficient criterion is provided that tells us when all 2(m) dichotomies over a given set of m patterns can be learned by a sign-constrained perceptron. We also show that uniformity of L(1) norms of input patterns is a sufficient condition for full representation power in the case where all weights are required to be nonnegative. Finally, we exhibit cases where the sign constraint of a perceptron drastically reduces its classification capability. Our theoretical analysis is complemented by computer simulations, which demonstrate in particular that sparse input patterns improve the classification capability of sign-constrained perceptrons.     

Performance surfaces of a single-layer perceptron

A perceptron learning algorithm may be viewed as a steepest-descent method whereby an instantaneous performance function is iteratively minimized. An appropriate performance function for the most widely used perceptron algorithm is described and it is shown that the update term of the algorithm is the gradient of this function. An example is given of the corresponding performance surface based on Gaussian assumptions and it is shown that there is an infinity of stationary points. The performance surfaces of two related performance functions are examined. Computer simulations that demonstrate the convergence properties of the adaptive algorithms are given.     

自适应高阶无迹增量卡尔曼滤波算法

在工程实际中,由于环境因素的影响、测量设备的不稳定性、模型和参数的选取不当等往往会对量测方程带来未知的系统误差.针对这一问题,提出了一种自适应高阶无迹增量卡尔曼滤波算法.首先,利用增量建模技术建立增量量测方程.其次,将其与高阶无迹卡尔曼滤波器相结合,并引入自适应加权因子对滤波发散进行抑制,发展出一种自适应增量滤波算法.计算机仿真实验表明,新算法能够成功消除这种未知的系统误差,提高估计精度和稳定性,具备良好的应用前景.     

Memory neuron networks for identification and control of dynamicalsystems

This paper discusses memory neuron networks as models for identification and adaptive control of nonlinear dynamical systems. These are a class of recurrent networks obtained by adding trainable temporal elements to feedforward networks that makes the output history-sensitive. By virtue of this capability, these networks can identify dynamical systems without having to be explicitly fed with past inputs and outputs. Thus, they can identify systems whose order is unknown or systems with unknown delay. It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory. The paper presents a preliminary analysis of the learning algorithm, providing theoretical justification for the identification method. Methods for adaptive control of nonlinear systems using these networks are presented. Through extensive simulations, these models are shown to be effective both for identification and model reference adaptive control of nonlinear systems.     

Fuzzy integral-based perceptron for two-class pattern classification problems

The single-layer perceptron with single output node is a well-known neural network for two-class classification problems. Furthermore, the sigmoid or logistic function is usually used as the activation function in the output neuron. A critical step is to compute the sum of the products of the connection weights with the corresponding inputs, which indicates the assumption of additivity among individual variables. Unfortunately, because the input variables are not always independent of each other, an assumption of additivity may not be reasonable enough. In this paper, the inner product can be replaced with an aggregation value obtained by a useful fuzzy integral by viewing each of the connection weights as a value of a λ-fuzzy measure for the corresponding variable. A genetic algorithm is then employed to obtain connection weights by maximizing the number of correctly classified training patterns and minimizing the errors between the actual and desired outputs of individual training patterns. The experimental results further demonstrate that the proposed method outperforms the traditional single-layer perceptron and performs well in comparison with other fuzzy or non-fuzzy classification methods.     

Nonlinear control structures based on embedded neural system models

This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper.