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.     

Process monitoring using auto-associative,feed-forward artificial neural networks

The potential of using artificially simulated neural networks as intelligent, adaptive process-monitoring devices is discussed. The investigation is considered as a method for automatic, intelligent exception reporting for quality control applications. The technique is also compared with the conventional statistical approaches of principal component analysis and Kohonen's feature map. The applications of the technique in aerospace and manufacturing environments are presented and a possible extension of the method to incorporate a diagnostic function is discussed.Seconded from Cheltenham and Gloucester College of Higher Education as a Royal Society/SERC Research Fellow at Smith's Industries Aerospace and Defence Systems, Bishop's Cleeve, Cheltenham, UK.     

稀土萃取分离过程的优化设定控制

针对稀土萃取分离生产过程的特点,将机理分析与神经网络技术相结合,给出了实现稀土萃取分离生产过程组份含量在线预测的软测量模型及其校正算法．提出了基于案例推理和软测量技术相结合的稀土萃取分离生产过程智能优化设定控制技术．将该技术应用于某公司HAB双溶剂萃取提钇分离生产过程,实现了萃取分离生产过程的优化控制和优化运行,取得了明显的应用成效．     

Intelligent stock trading system with price trend prediction and reversal recognition using dual-module neural networks

This article presents an intelligent stock trading system that can generate timely stock trading suggestions according to the prediction of short-term trends of price movement using dual-module neural networks(dual net). Retrospective technical indicators extracted from raw price and volume time series data gathered from the market are used as independent variables for neural modeling. Both neural network modules of thedual net learn the correlation between the trends of price movement and the retrospective technical indicators by use of a modified back-propagation learning algorithm. Reinforcing the temporary correlation between the neural weights and the training patterns, dual modules of neural networks are respectively trained on a short-term and a long-term moving-window of training patterns. An adaptive reversal recognition mechanism that can self-tune thresholds for identification of the timing for buying or selling stocks has also been developed in our system. It is shown that the proposeddual net architecture generalizes better than one single-module neural network. According to the features of acceptable rate of returns and consistent quality of trading suggestions shown in the performance evaluation, an intelligent stock trading system with price trend prediction and reversal recognition can be realized using the proposed dual-module neural networks.     

EVOLVING NEURAL-SYMBOLIC SYSTEMS GUIDED BY ADAPTIVE TRAINING SCHEMES: APPLICATIONS IN FINANCE

The article presents a hybrid and adaptive intelligent methodology, based on neural logic networks and grammar-guided genetic programming. The aim of the study is to demonstrate how to generate efficient neural logic networks with the aid of genetic programming methods trained adaptively through an innovative scheme. The proposed adaptive training scheme of the genetic programming mechanism leads to the generation of high-diversity solutions and small-sized individuals. The overall methodology is advantageous due to the adaptive training scheme proposed for offering both accurate and interpretable results in the form of expert rules. Moreover, a sensitivity analysis study is provided within the article, comparing the performance of the proposed evolutionary neural logic networks methodology with well-known competitive inductive machine learning approaches. Two financial domains of application have been selected to demonstrate the capabilities of the proposed methodology: (a) classification of credit applicants for consumer loans of a German bank and (b) the credit-scoring decision-making process in an Australian bank. Results seem encouraging since the proposed methodology outperforms a number of competitive existing statistical and intelligent methodologies, while it also produces handy decision rules, short in length and transparent in meaning and use.     

基于BP神经网络的自适应控制

本文利用ＢＰ神经网络对被控对象进行在线辨识和控制。为实现自适应控制，本文对ｓｐｅｃｉａｌｉｓｅｄ ｌｅａｒｎｉｎｇ算法进行了改进，在此基础上，本文还提出了一种基于ＢＰ网络的自适应ＰＩＤ控制器。     

基于门控网络的军事装备控制指令语音识别研究

军事装备无感控制是军事装备智能化建设进程中的一个重要研究方向,其中语音控制技术作为无人装备无感控制手段的关键组成部分,受到了越来越多的重视。为完成军事装备语音控制任务,设计一种基于门控网络的中文语音识别网络,并构建军事装备控制指令数据集,实现基于控制指令语音识别技术的军事装备控制。在传统卷积神经网络的结构基础上引入深度残差门控卷积网络,提高识别网络的准确性,同时通过多途径构建军事装备控制指令数据集,设计一套针对军事装备无感控制的语音识别方案。实验结果表明,该语音识别网络军事语音控制指令识别率可达87%,外接语言模型后可达92%,语音识别准确率高、误差率低,可完成军事装备的语音控制任务。     

Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process

The integration of statistical process control and engineering process control has been reported as an effective way to monitor and control the autocorrelated process. However, because engineering process control compensates for the effects of underlying disturbances, the disturbance patterns become very hard to recognize, especially when various abnormal control chart patterns are mixed and co-existed in the engineering process. In this study, a new control chart pattern recognition model which integrates multivariate adaptive regression splines and recurrent neural network is proposed to not only address the problem of feature selection (i.e., lagged process measurements) but also improve the pattern recognition accuracy. The performance of the proposed method is evaluated by comparing the recognition results of multivariate adaptive regression splines and recurrent neural network with the results of four competing approaches (multivariate adaptive regression splines-extreme learning machine, multivariate adaptive regression splines-random forest, single recurrent neural network, and single random forest) on the simulated individual process data. The experimental study shows that the proposed multivariate adaptive regression splines and recurrent neural network approach can not only solve the problem of variable selection but also outperform other competing models. Moreover, according to the lagged process measurements selected by the proposed approach, lagged observations that exerted significant impact on the construction of the control chart pattern recognition model can be identified successfully. This study has significant implications for research and practice in production management and provides a valuable reference for manufacturing process managers to better understand and develop strategies for control chart pattern recognition.     

交流调速系统的神经网络自适应控制

针对交流变频调速系统，提出一种用于控制未知系统模型的改进的神经网络自适应控制方案。该方法采用一个三层BP网络辩识交流调速系统的特性，用另一个CMAC神经网络作为自适应控制器。实验仿真证明该控制方法能克服传统交流调速中常规控制不能适应工况变化的弊端，能明显的提高系统的性能，具有实际应用价值。     

智能传感器侦察网络中的目标声信号识别算法

针对智能传感器侦察网络中的地面目标识别问题,提出了一种基于智能计算方法的地面目标声信号识别算法。基于智能计算方法设计识别系统,直接利用信号特征的变化范围作为分类特征,并结合能够处理定性输入的粗神经网络分类算法,有效地克服目标信号的不确定性问题,提高识别系统的识别率和稳定性。     

Adaptive manipulator trajectory control using neural networks

A unified study of adaptive control and neural network based control schemes for the trajectory tracking problem of robot manipulators is presented. Efficacy of parametrized adaptive algorithms in compensating the structured uncertainties in robot dynamics is verified through extensive simulation. The ability of neural networks to provide a robust adaptive framework in the presence of both structured and unstructured uncertainties is investigated. A case study is carried out in support of a parametrized adaptive scheme using neural networks. Simulation results clearly indicate that the neural network based adaptive controller achieves better tracking in the presence of parametric uncertainties as well as unmodelled effects compared to the simple direct adaptive scheme.     

An Adaptive, Intelligent Control System for Slag Foaming

Slag foaming is a steel-making process that has been shown to improve the efficiency of electric arc furnace plants. Unfortunately, slag foaming is a highly dynamic process that is difficult to control. This paper describes the development of an adaptive, intelligent control system for effectively manipulating the slag foaming process. The level-2 intelligent control system developed is based on three techniques from the field of computational intelligence (CI): (1) fuzzy logic, (2) genetic algorithms, and (3) neural networks. Results indicate that the computer software architecture presented in this paper is suitable for effectively manipulating complex engineering systems characterized by relatively slow process dynamics like those of a slag foaming operation.     

Neuron-Like Approach to Speech Recognition

In this paper, we present a new approach to speech recognition based on A. Zhdanov’s biomorphic neuron-like networks, which is also known as the autonomous adaptive control (AAC) method. In contrast to artificial neural networks (ANNs), a neuron in the AAC method is itself a self-learning pattern recognition system. We attempt to build a speech recognition system as a construction of such neurons without a program component. If this attempt is successful, then we will be able to simulate the natural principle of speech recognition not only in a program way but also via parallel hardware implementations. We understand the speech recognition problem as one of the speech processes in natural nervous systems that is to be simulated.     

非线性互联系统的分散混合自适应智能控制

针对一类高阶互联MIMO非线性系统，利用TS模糊系统和神经网络的通用逼近能力，在神经网络控制器中引入模糊基函数，提出一种分散混合自适应智能控制器设计的新方案．基于等价控制思想，设计分散自适应控制器，无需计算TS模型．通过对不确定项进行自适应估计，取消了其存在已知上界的假设．通过理论分析，证明了闭环智能控制系统所有信号有界，跟踪误差收敛到零．     

Engineering applications of neural networks

This paper describes several prototypical applications of neural network technology to engineering problems. The applications were developed by the authors as part of a graduate-level course taught at the University of Illinois at Urbana-Champaign by the first author (now at Carnegie Mellon University). The applications are: an adaptive controller for building thermal mass storage; an adaptive controller for a combine harvester; an interpretation system for non-destructive evaluation of masonry walls; a machining feature recognition system for use in process planning; an image classification system for classifying land coverage from satellite or high-altitude images; and a system for designing the pumping strategy for contaminated groundwater remediation. These applications are representative of many of the engineering problems for which neural networks are applicable: adaptive control, feature recognition, and design.     

An intelligent composite system for statistical process control

Combining statistical process control, artificial neural networks and an expert system for the intelligent analysis and control of a plastic extruder facility is described. Statistical methodology is compared and contrasted to the exploratory neural network technique, which learns to relate and classify dependent production variables based on measurements taken on-line during the process. Integrating the neural network analysis into a composite control system using an expert system is presented.     

Neural simulation of a prototype keyboard assembly cell with adaptive control

This paper describes the integration of a feedforward neural network with a real-time adaptive control technique to model an assembly cell for computer keyboards. The simulation model is able to undertake real-time forecasting of the expected behaviour of the cell, and also able to display in real time the current status of the assembly cell. The potential of integrating artificial neural networks into simulation together with an on-line real-time interface between the simulation model and the real-world system is investigated in this study. A simulation model was built in SIMAN with a neural network written in NeuralWork Professional II Plus embedded in the model to allow training and testing of the network. The control of the cell was achieved through a third-party program written in C that interfaces directly to the cell.     

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。     

Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming

The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy. The aim of the study is to obtain classification schemes able to predict business failure. Previous attempts to form efficient classifiers for the same problem using intelligent or statistical techniques are discussed throughout the paper. The application of neural logic networks by means of genetic programming is proposed. This is an advantageous approach enabling the interpretation of the network structure through set of expert rules, which is a desirable feature for field experts. These evolutionary neural logic networks are consisted of an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability.     

Adaptive control of a CSTR with a neural network model

An adaptive control algorithm with a neural network model, previously proposed in the literature for the control of mechanical manipulators, is applied to a CSTR (Continuous Stirred Tank Reactor). The neural network model uses either radial Gaussian or “Mexican hat” wavelets as basis functions. This work shows that the addition of linear functions to the networks significantly improves the error convergence when the CSTR is operated for long periods of time in a neighborhood of one operating point, a common scenario in chemical process control. Then, a quantitative comparative study based on output errors and control efforts is conducted where adaptive controllers using wavelets or Gaussian basis functions and PID controllers (IMC tuning with fixed parameters and self tuning PID) are compared. From this comparative study, the practicality and advantages of the adaptive controllers over fixed or adaptive PID control is assessed.