聚合物分子量分布的多变量动态系统模型

为了解决聚合产品分子量分布控制的难题,将神经网络引入对其进行了无需任何系统内部先验知识的黑箱建模。所使用的神经网络是由B样条神经网络和非线性递归神经网络(DRNN)组合而成,并使用误差反传算法对网络进行训练和学习,从而建立了多变量动态系统的分子量分布模型。在模型建立中将控制变量与分布参数的函数关系利用非线性递归神经网络描述,分子量分布函数使用B样条神经网络表示,仿真研究结果证明该方法取得了预期的建模效果,具有一定的推广实用价值。     

基于改进BP神经网络辨识的DMFC建模研究

针对直接甲醇燃料电池（DMFC）系统过于复杂，难以建模的特点，该文试图绕开DMFC的内部复杂性，基于实验数据。利用神经网络逼近任意复杂非线性函数的能力，将神经网络辨识方法应用到DMFC这种高度非线性系统的建模。以1000组电池电压、电流密度实验数据作为训练样本，采用基于LM算法的改进BP神经网络，建立了不同温度下电池电压-电流密度动态响应模型。仿真结果表明这种方法是可行的，建立的模型精度较高，它使得设计DMFC的实时控制器成为可能。     

基于神经网络辨识的熔融碳酸盐燃料电池（MCFC）建模

针对熔融碳酸盐燃料电池（MCFC）电堆系统过于复杂,难以建模以及已建立的模型过于复杂,难以满足工程上对MCFC系统控制设计特别是实时控制的需要,该文试图绕开MCFC的内部复杂性,提出利用神经网络具有逼近任意复杂非线性函数的能力,将神经网络辨识方法应用到MCFC这种高度非线性系统的建模。以燃料气和氧化剂气体的流速为输入量,MCFC电堆的温度响应为输出量,根据输入输出数据用神经网络辨识建立MCFC电堆系统的温度模型,给出了辨识系统的结构及改进BP算法。仿真结果证明了这种方法的可行性,建立的模型精度较高,它使得设计MCFC的实时控制器成为可能。     

基于一种改进的BP神经网络光伏电池建模

由于光伏电池具有高度非线性特性，难以建模，而传统的数学模型难以满足光伏控制系统设计和应用的要求。该文利用神经网络具有逼近任意复杂非线性函数的能力，将神经网络技术应用到光伏阵的建模中，避开了该模块内部的复杂性。模型以太阳能日照、温度以及负载电压作为神经网络辨识模型的输入量，光伏阵输出电流为输出量，采用改进型BP算法，建立了光伏电池的动态响应模型，然后预测了最大功率点。文中给出模型的结构，训练步骤和仿真结果。仿真结果表明，方法可行，建立的模型精度较高，从而为设计光伏实时控制系统奠定了基础。     

光滑拉格朗日神经网络解决非光滑最优化问题

针对目标函数是局部Lipschitz函数,其可行域由一组等式约束光滑凸函数组成的非光滑最优化问题,通过引进光滑逼近技术将目标函数由非光滑函数转换成相应的光滑函数,进而构造一类基于拉格朗日乘子理论的神经网络,以寻找满足约束条件的最优解。证明了神经网络的平衡点集合是原始非光滑最优化问题关键点集合的一个子集;当原始问题的目标函数是凸函数时,最小点集合与神经网络的平衡点集合是一致的。通过仿真实验验证了理论结果的正确性。     

管式聚合反应器温度分布的动态建模与广义PI控制

针对阳离子聚合反应器的温度分布建模与控制问题,提出了一种基于B样条神经网络的广义PI控制方法.首先采用B样条复合网络建立分布函数的动态和静态模型,并基于该模型,将分布函数的跟踪问题等效为动态权值向量的时间域跟踪问题.最后给出一种新型的广义PI控制方法,实现对给定温度分布的跟踪控制.同时,为了更好地抑制未知干扰、参数摄动以及模型不匹配等问题,模型权值状态、模型输出与实测温度分布所对应的权值误差都被引入到反馈控制回路,因此能够大大增强系统的鲁棒性与抗干扰能力.仿真结果表明该方法的可行性.     

基于聚类算法的支持向量回归建模的新策略

针对支持向量机对时变的样本集采用单一模型建模困难的问题，提出了一种新的学习策略．首先，使用自组织映射(SOM)神经网络和k-means聚类算法对初始样本集合进行聚类．然后，针对每个聚类数据集合，通过最优加权组合不同核函数的支持向量回归模型建立最终的模型．实验表明，采用这种学习策略的建模精度要优于单一支持向量回归建模方法．     

基于正交多项式函数的神经网络及其性质研究

神经网络的非线性逼近能力的研究是神经网络研究中的一个热点问题。该文提出了基于正交多项式函数的神经网络构造理论,以此为基础提出了基于正交多项式函数的神经网络的构造方法,利用Stone-Weierstrass定理从理论上证明了基于正交多项式函数的神经网络具有能以任意精度逼近任意紧集上的连续函数的全局逼近性质,最后,提出了基于正交多项式函数的神经网络的选择和评价方法,研究表明,在一定条件下,当选择Chebyshev多项式时,所构造出的神经网络性能最优。     

自适应神经网络模糊推理系统最优参数的研究

模糊规则的提取和隶属度函数的学习是模糊系统设计中重要而困难的问题。自适应神经网络模糊推理系统（ANFIS）能基于数据建模，无须专家经验，自动产生模糊规则和调整隶属度函数。在建立一个初始系统进行训练时，其隶属度函数的类型、隶属度函数的数日以及训练次数都是待定的，这三个参数的选择直接影响系统训练后的效果，它们的确定方法有待研究。该文应用自适应神经网络模糊推理系统的方法对一个典型系统进行建模仿真，并阐述这三个参数的寻优方法。     

基于模糊模型和神经网络的某扫雷犁系统建模

在实现扫雷犁系统准确位置控制的研究中,由于扫雷犁电液伺服系统固有的流量-压力特性等非线性因素,采用传统机理建模方法难以获得其精确模型,研究了系统的两种智能建模方法,即模糊建模和神经网络建模。模糊建模方法采用基于GK聚类算法的TS模糊模型,神经网络建模中采用了基于正交最小二乘算法的径向基函数神经网络。通过对扫雷犁电液伺服系统进行的建模实验仿真,分析了两方法的建模性能并与其他建模方法进行了对比,研究结果验证了所提出两种建模方法的有效性。     

Gray-box modeling and control of polymer molecular weight distribution using orthogonal polynomial neural networks

A method of modeling and control on polymer molecular weight distribution (MWD) is presented in this paper. An orthogonal polynomial feedforward neural network (OPFNN) and a recurrent neural network (RNN) are combined to model the shape of MWD. In this combined neural networks, the weights of OPFNN are equivalent to moments of MWD through a linear transformation, when the polynomial used as the basis function of OPFNN satisfies some requirements. So the weights are given practical feature, and terms the neural network model a gray-box model. Then the requirements of polynomial are deduced. After modeling, an optimal control scheme is discussed on tracking the desired MWD during the polymerization process. The modeling error is added into the performance function to improve the control effect. The modeling and control method is tested on styrene polymerization reacted in CSTR, and simulation results proved the effectiveness of the method.     

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.     

Nonlinear modeling of MCFC stack based on RBF neural networks identification

Modeling molten carbonate fuel cells (MCFC) is very difficult and the most existing models are based on conversation laws which are too complicated to be used to design a control system. This paper presents an application of radial basis functions (RBF) neural networks identification to develop a nonlinear temperature model of MCFC stack. The temperature characters of MCFC stack are briefly analyzed. A summary of RBF neural networks modeling of MCFC is introduced. The simulation tests reveal that it is feasible to establish the model of MCFC stack using RBF neural networks identification. The modeling process avoids using complicated differential equations to describe the stack and the neural networks model developed can be used to predict the temperature responses online which makes it possible to design online controller of MCFC stack.     

Intelligent control of braking process

Intelligent modeling, prediction and control of the braking process are not an easy task if using classical modeling techniques, regarding its complexity. In this paper, the new approach has been proposed for easy and effective monitoring, modeling, prediction, and control of the braking process i.e. the brake performance during a braking cycle. The context based control of the disc brake actuation pressure was used for improving the dynamic control of braking process versus influence of the previous and current values of the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. For these purposes, two different dynamic neural models have been developed and integrated into the microcontroller. Microcontrollers are resource intensive and cost effective platforms that offer possibilities to associate with commonly used artificial intelligence techniques. The neural models, based on recurrent dynamic neural networks, are implemented in 8-bit CMOS microcontroller for control of the disc brake actuation pressure during a braking cycle. The first neural model was used for modeling and prediction of the braking process output (braking torque). Based on such acquired knowledge about the real brake operation, the inverse neural model has been developed which was able to predict the brake actuation pressure needed for achieving previously selected (desired) braking torque value in accordance with the previous and current influence of the pressure, speed, and the brake interface temperature. Both neural models have had inherent abilities for on-line learning and prediction during each braking cycle and an intelligent adaptation to the change of influences of pressure, speed, and temperature on the braking process.     

Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln

Artificial neural networks (ANNs) and neuro-fuzzy systems (NFSs) have been widely used in modeling and control of many practical industrial nonlinear processes. However, most of them have concentrated on single-output systems only. In this paper, we present a comparative study using ANNs and co-active neuro-fuzzy inference system (CANFIS) in modeling a real, complicated multi-input–multi-output (MIMO) nonlinear temperature process of roller kiln used in ceramic tile manufacturing line. Using this study, we prove that CANFIS is better suited for modeling the temperature process in control phase. After that, a neural network (NN) controller has been developed to control the above mentioned temperature process due to a feedback control diagram. The designed controller performance is tested by a Visual C++ project and the resulting numerical data shows that this controller can work accurately and reliably when the roller kiln set-point temperature set changes.     

Bioreactor temperature profile controller using inverse neural network (INN) for production of ethanol

This paper presents the use of inverse neural networks (INN) for temperature control of a biochemical reactor and its effect on ethanol production. The process model is derived indicating the relationship between temperature, pH and dissolved oxygen. Using fundamental model obtained data sets; an inverse neural network has been trained using the back-propagation learning algorithm. Two types of temperature profile are used to compare the performance of the INN and conventional PID controllers. These controllers have been simulated in MATLAB for a quantitative comparison. The results obtained by the neural network based INN controller and by the PID controller are presented and compared. There is an improvement in the performance of INN controller in settling time and dead time and steady state error over the PID controller.     

典型人工神经网络的结构、功能及其在智能系统中的应用

人工神经网络已在各个领域得到广泛的应用, 尤其是在智能系统中的非线性建模及其控制器的设计、模式分类与模式识别、联想记忆和优 化计算等方面更是得到人们的极大关注．本文从网络在智能系统中建模及控制器设计的具体 训练结构入手,详细介绍了BP网络在系统控制中的典型应用方式,并根据不同网络所具有的 功能,从性能对比的角度对人工神经网络在上述各方面的应用给予综述．     

Neural Network Structures for Optimal Control of LPCVD Reactors

In this paper, a new approach of LPCVD reactor modelling and control is presented, based on the use of neural networks. We first present the development of a hybrid networks model of the reactor. The objective is to provide a simulation model which can be used to compute online the film thickness on each wafer. In the second section, the thermal control of a LPCVD reactor is studied. The objective is to develop a multivariable controller to control a space- and time-varying temperature profile inside the reactor. A neural network is designed using a methodology based on process inverse dynamics modelling. Good control results have been obtained when tracking space-time temperature profiles inside the LPCVD reactor pilot plant. Finally, global software is elaborated to achieve film thickness control in an experimental LPCVD reactor pilot plant, in order to get a defined and uniform deposition thickness on the wafers all along the reactor. Experimental results are presented which confirm the efficiency of the optimal control strategy.     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.