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。     

Back-propagation neural networks for modeling complex systems

In complex engineering systems, empirical relationships are often employed to estimate design parameters and engineering properties. A complex domain is characterized by a number of interacting factors and their relationships are, in general, not precisely known. In addition, the data associated with these parameters are usually incomplete or erroneous (noisy). The development of these empirical relationships is a formidable task requiring sophisticated modeling techniques as well as human intuition and experience. This paper demonstrates the use of back-propagation neural networks to alleviate this problem. Backpropagation neural networks are a product of artificial intelligence research. First, an overview of the neural network methodology is presented. This is followed by some practical guidelines for implementing back-propagation neural networks. Two examples are then presented to demonstrate the potential of this approach for capturing nonlinear interactions between variables in complex engineering systems.     

Neural network synthesis for thermal processes

Neural networks are relatively new and highly attractive tools for modelling complex systems. The main feature of neural networks is their inherent plasticity which enables them to fit virtually any nonlinear function provided they have a sufficient number of parameters. Neural networks are a general class of nonlinear systems. Neural models can be used advantageously to model the dynamic behaviour of physical processes. In this paper, feedforward neural networks are used for modelling of dynamic thermal processes. The synthesis of neural networks is directly associated with the minimization of an objective function normally defined as the square of the difference between the output of the process being modelled and the output predicted by the network. Learning schemes are used for the evaluation of the connection weights of the feedforward neural network. In this paper, the dynamic modelling of several thermal processes using feedforward neural networks is presented. In one example, the identified neural model of the inverse of the plant is used as a controller.     

人工神经网络GTAW建模及控制

给出一种用于钨极气体保护电弧焊（ＧＴＡＷ）建及控制的人工神经网络（ＡＮＮ），重点论述利用ＡＮＮ建立焊接参数模型的方法以及在熔深控制方面的应用，通过实验证明，所提出的智能方法具有良好的系统控制性能。     

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.     

Hidden control neural architecture modeling of nonlinear timevarying systems and its applications

The application of neural networks to modeling time-invariant nonlinear systems has been difficult for complicated nonstationary signals, such as speech, because the networks are unable to characterize temporal variability. This problem is addressed by proposing a network architecture, called the hidden control neural network (HCNN), for modeling signals generated by nonlinear dynamical systems with restricted time variability. The mapping implemented by a multilayered neural network is allowed to change with time as a function of an additional control input signal. The network is trained using an algorithm based on ;backpropagation' and segmentation algorithms for estimating the unknown control together with the network's parameters. Application of the network to the segmentation and modeling of a signal produced by a time-varying nonlinear system, speaker-independent recognition of spoken connected digits, and online recognition of handwritten characters demonstrates the ability of the HCNN to learn time-varying nonlinear dynamics and its potential for high-performance recognition of signals produced by time-varying sources.     

Dynamic neural controllers for induction motor

The paper reports application of recently developed adaptive control techniques based on neural networks to the induction motor control. This case study represents one of the more difficult control problems due to the complex, nonlinear, and time-varying dynamics of the motor and unavailability of full-state measurements. A partial solution is first presented based on a single input-single output (SISO) algorithm employing static multilayer perceptron (MLP) networks. A novel technique is subsequently described which is based on a recurrent neural network employed as a dynamical model of the plant. Recent stability results for this algorithm are reported. The technique is applied to multiinput-multioutput (MIMO) control of the motor. A simulation study of both methods is presented. It is argued that appropriately structured recurrent neural networks can provide conveniently parameterized dynamic models for many nonlinear systems for use in adaptive control.     

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.     

污水处理过程的递阶神经网络建模

针对污水处理过程的多变量和多非线性子系统的串级结构特点, 提出了一种基于活性污泥过程机理的递 阶神经网络建模方法.该方法将神经网络与过程机理模型以串级方式连接, 以神经网络辨识活性污泥过程模型中的非线性组分反应速率. 分析各子过程建模误差的关系, 给出了模型的稳定学习算法和稳定性理论分析. 最后通过某污水处理厂生化脱氮过程实际运行数据的实验表明所提出的建模方法是有效的.     

发酵过程的多模型融合建模算法

提出了利用多模型融合技术进行发酵过程建模的新方法， 该方法能够将在线参数和离线参数同时用于建模中． 首先给出了多模型融合建模算法框架， 并描述了基于自适应模糊神经网络和模糊推理技术两个参与融合的子模型的建立方法. 采用三个非线性函数分别运用GMDH-PTSV算法、傅里叶神经网络和多模型融合建模算法进行建模精度比较. 最后给出了多模型融合建模算法在青霉素发酵过程中应用的结果．     

An investigation of neural networks for linear time-series forecasting

This study examines the capability of neural networks for linear time-series forecasting. Using both simulated and real data, the effects of neural network factors such as the number of input nodes and the number of hidden nodes as well as the training sample size are investigated. Results show that neural networks are quite competent in modeling and forecasting linear time series in a variety of situations and simple neural network structures are often effective in modeling and forecasting linear time series.Scope and purposeNeural network capability for nonlinear modeling and forecasting has been established in the literature both theoretically and empirically. The purpose of this paper is to investigate the effectiveness of neural networks for linear time-series analysis and forecasting. Several research studies on neural network capability for linear problems in regression and classification have yielded mixed findings. This study aims to provide further evidence on the effectiveness of neural network with regard to linear time-series forecasting. The significance of the study is that it is often difficult in reality to determine whether the underlying data generating process is linear or nonlinear. If neural networks can compete with traditional forecasting models for linear data with noise, they can be used in even broader situations for forecasting researchers and practitioners.     

基于神经网络的多变量发酵过程自适应控制

运用非线性系统的线性化方法与神经网络在线辨识技术，提出了一种基于神经网络 的多变量自适应控制策略．提出的控制策略，当过程模型缺乏足够的先验知识时，对多变量 非线性连续发酵过程取得了良好的控制性能．仿真结果表明，提出的自适应控制方法能够适 应过程模型的不确定性和参数的时变性，具有较强的鲁棒性．并且通过对比分析得出，基于 微分几何理论的输入输出线性化解耦控制方案，由于控制器的设计依赖于过程模型，对模型 参数的变化很敏感，应用在发酵过程的非线性控制中，控制精度较低，鲁棒性较差．     

脉冲GTAW熔池动态过程模糊神经网络建模与控制

展示了模糊推理与神经网络结合在脉冲GTAW熔池动态过程智能控制中的应用研究结果.建立了脉冲GTAW平板对接动态过程特征:正反面熔池的最大宽度、长度与面积等参数的神经网络模型,基于实验数据采用模糊辨识方法提取焊接过程的模糊控制规则,进而设计了具有自学习适应能力的模糊神经网络控制器.建立了脉冲GTAW熔池动态过程智能控制系统,焊接实验验证了所设计的模糊神经网络控制器具有智能控制效果.     

基于多层局部回归神经网络的多变量非线性系统预测控制

以罐式搅拌反应器为例,针对复杂多变量系统的强耦合性、非线性、时变性等问题,研究了多变量非线性系统的预测控制及改善控制性能的方法,采用多层局部回归神经网络离线建立预测模型,以偏差补偿和模型修正相结合的方式对预测模型进行误差补偿,以要线校正用于预测控制,通过对性能指标中的偏差项负指数加权,进一步改善预测控制性能,住址结果表明了控制算法的有效性。     

Local model network identification with Gaussian processes.

A Bayesian Gaussian process (GP) modeling approach has recently been introduced to model-based control strategies. The estimate of the variance of the predicted output is the most useful advantage of GPs in comparison to neural networks (NNs) and fuzzy models. However, the GP model is computationally demanding and nontransparent. To reduce the computation load and increase transparency, a local linear GP model network is proposed in this paper. The proposed methodology combines the local model network principle with the GP prior approach. A novel algorithm for structure determination and optimization is introduced, which is widely applicable to the training of local model networks. The modeling procedure of the local linear GP (LGP) model network is demonstrated on an example of a nonlinear laboratory scale process rig.     

非线性系统RBF网在线建模的资源优化网络方法

提出了一种RBF网非线性动态系统在线建模的资源优化网络(RON)方法.RON在资源分配网络的学习过程中引入了滑动窗口和网络结构在线优化的思想,使网络能根据最近一段时间内的误差信息自动实现网络结构优化,从而使RBF网既能在线适应对象的变化,又能使网络规模维持在较小水平,并保证了网络的泛化能力.使用滑动窗口技术使RON对学习参数变化具有较好的鲁棒性,并更易收敛.三个标准例子演示了算法的有效性.