Dynamics identification and control of nonlinear MIMO coupled plant using supervised neural gas and comparison with recurrent neural controller

The dynamics identification and subsequent control of a nonlinear system is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function, demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling. Furthermore, the algorithm is applied to control a nonlinear multi-input multi-output system composed of tanks. In addition, this plant is a coupled system where the manipulated input variables are influencing all the output variables. The aim of the work is to demonstrate that the supervised neural gas algorithm is able to obtain linear models to be used in a state space design scenario to control nonlinear coupled systems and guarantee a robust control method. The results are compared with the common approach of using a recurrent neural controller trained with a dynamic backpropagation algorithm. Regarding the steady-state errors in disturbance rejection, reference tracking and sensitivity to simple process changes, the proposed approach shows an interesting application to control nonlinear plants.

     

汽车轮速信号BP网络结构优化设计

本文将神经网络方法引入到轮速信号处理之中,以实际采集的噪声信号作为输入,以小波滤波信号作为标准的输出,设计一个3层BP网络,根据结构的优化设计新方法,建立相应的神经网络模型;以所采集数据中的一部分作为学习样本,对所建神经网络模型进行训练、仿真;以采集数据中的其余部分作为检验数据。仿真结果表明,该模型能以很小的误差逼近标准的滤波输出。     

Using function approximation to analyze the sensitivity of MLP withantisymmetric squashing activation function

Sensitivity analysis on a neural network is mainly investigated after the network has been designed and trained. Very few have considered this as a critical issue prior to network design. Piche's statistical method (1992, 1995) is useful for multilayer perceptron (MLP) design, but too severe limitations are imposed on both input and weight perturbations. This paper attempts to generalize Piche's method by deriving an universal expression of MLP sensitivity for antisymmetric squashing activation functions, without any restriction on input and output perturbations. Experimental results which are based on, a three-layer MLP with 30 nodes per layer agree closely with our theoretical investigations. The effects of the network design parameters such as the number of layers, the number of neurons per layer, and the chosen activation function are analyzed, and they provide useful information for network design decision-making. Based on the sensitivity analysis of MLP, we present a network design method for a given application to determine the network structure and estimate the permitted weight range for network training.     

Application of neural networks in forecasting engine systems reliability

This paper presents a comparative study of the predictive performances of neural network time series models for forecasting failures and reliability in engine systems. Traditionally, failure data analysis requires specifications of parametric failure distributions and justifications of certain assumptions, which are at times difficult to validate. On the other hand, the time series modeling technique using neural networks provides a promising alternative. Neural network modeling via feed-forward multilayer perceptron (MLP) suffers from local minima problems and long computation time. The radial basis function (RBF) neural network architecture is found to be a viable alternative due to its shorter training time. Illustrative examples using reliability testing and field data showed that the proposed model results in comparable or better predictive performance than traditional MLP model and the linear benchmark based on Box–Jenkins autoregressive-integrated-moving average (ARIMA) models. The effects of input window size and hidden layer nodes are further investigated. Appropriate design topologies can be determined via sensitivity analysis.     

Application of neural networks to detection of medical fraud

A multi-layer perceptron (MLP) network was trained to classify the practice profiles of a sample of medical general practitioners who had been classified by expert consultants into four classes ranging from having normal to having abnormal profiles. This method follows the two-class neural network classification of medical practice profiles developed at the Health Insurance Commission in 1990. A technique based on the probabilistic interpretation of the output of the neural network was used to see if it improved the performance of the MLP given the extent of noise (i.e. inconsistencies) in the experts' classifications. Kohonen's Self-Organising Map was also applied to analyse the consultants' classifications and it was found that an approach which combined the four classes into two was a more appropriate way to represent the classification data. The MLP network was then retrained using a two-class classification and a high agreement rate between the classifications of the MLP and the classifications of consultants was achieved.     

Development of a neural network based integrated control system of 120 ton/h capacity boiler

An integrated control system based on artificial neural network (ANN) is presented in this paper to control a 120 ton/h capacity boiler of the Zia Fertilizer Company Limited (ZFCL), Ashuganj, Bangladesh. The process inverse dynamic modelling technique is applied to design the proposed controller. A multilayer feed-forward neural network is trained to identify the unknown inverse dynamic model of the boiler plant by a well known learning algorithm called backpropagation. The training data were collected from the history file of ZFCL. A new software controller is then developed for integrated control system of the ZFCL boiler using the weights of the trained network. Both the training mode and running mode of the developed controller are presented in this paper. The controller output is also converted into electrical signal using pulse width control technique. The generated signal is used for on-line regulation of the control valve through the parallel port of the computer. The developed controller is tested by using the boiler input–output data that are not used during the training. The output response and performance of the developed controller is compared with those of the existing PID controller of the plant.     

Robust neural network tracking controller using simultaneous perturbation stochastic approximation

This paper considers the design of robust neural network tracking controllers for nonlinear systems. The neural network is used in the closed-loop system to estimate the nonlinear system function. We introduce the conic sector theory to establish a robust neural control system, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the neural network. The neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed neural control system guarantees closed-loop stability of the estimation system, and a good tracking performance. The performance improvement of the proposed system over existing systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against system disturbance.     

An on-line wastewater quality predication system based on a time-delay neural network

The biological treatment process in a wastewater treatment system is a very complex process. The efficiency of the treatment is usually measured by laboratory tests, which typically take five days. In this paper, a time-delay neural network (TDNN) modeling method is proposed for predicting the treatment results. As the first step, a sensitivity analysis performed on a multi-layer perceptron (MLP) network model is used to reduce the input dimensions of the model. Then a TDNN model is further used to improve the performance of the original MLP network model. Subsequently, an on-line prediction and model-updating strategy is proposed and implemented. Simulations using industrial process data show that the prediction accuracy can be improved by the on-line model updating.     

Sensitivity analysis of multilayer perceptron to input and weightperturbations

An important issue in the design and implementation of a neural network is the sensitivity of its output to input and weight perturbations. In this paper, we discuss the sensitivity of the most popular and general feedforward neural networks-multilayer perceptron (MLP). The sensitivity is defined as the mathematical expectation of the output errors of the MLP due to input and weight perturbations with respect to all input and weight values in a given continuous interval. The sensitivity for a single neuron is discussed first and an analytical expression that is a function of the absolute values of input and weight perturbations is approximately derived. Then an algorithm is given to compute the sensitivity for the entire MLP. As intuitively expected, the sensitivity increases with input and weight perturbations, but the increase has an upper bound that is determined by the structural configuration of the MLP, namely the number of neurons per layer and the number of layers. There exists an optimal value for the number of neurons in a layer, which yields the highest sensitivity value. The effect caused by the number of layers is quite unexpected. The sensitivity of a neural network may decrease at first and then almost keeps constant while the number increases.     

神经网络在软件失效预测中的性能比较和研究

在软件开发的早期预测有失效倾向的软件模块,能够极大地提高软件的质量.软件失效预测中的一个普遍问题是数据中噪声的存在.神经网络具有鲁棒性而且对噪声有很强的抑制能力.不同结构的神经网络在训练算法和应用领域都有差异.该文主要就软件失效预测这个应用领域叙述几种适用的网络,并比较这几种网络在训练结果和性能上的差异.上述方法在SDH通信软件的失效预测中得到了成功的应用.试验结果显示虽然MLP、PNN、LVQ网络都能解决这类模式分类问题,但是只有MLP网络训练结果比较稳定,在不同的数据集上训练出的网络都有很好的预测效果.     

Aircraft class identification based on take-off noise signal segmentation in time

Aircraft noise is one of the most uncomfortable kinds of sounds. That is why many organizations have addressed this problem through noise contours around airports, for which they use the aircraft type as the key element. This paper presents a new computational model to identify the aircraft class with a better performance, because it introduces the take-off noise signal segmentation in time. A method for signal segmentation into four segments was created. The aircraft noise patterns are extracted using an LPC (Linear Predictive Coding) based technique and the classification is made combining the output of four parallel MLP (Multilayer Perceptron) neural networks, one for each segment. The individual accuracy of each network was improved using a wrapper feature selection method, increasing the model effectiveness with a lower computational cost. The aircraft are grouped into classes depending on the installed engine type. The model works with 13 aircraft categories with an identification level above 85% in real environments.     

ANN-BASED SYNCHRONOUS GENERATOR EXCITATION FOR TRANSIENT STABILITY ENHANCEMENT AND VOLTAGE REGULATION

Control of the synchronous generator, also referred to as an alternator, has always remained very significant in power system operation and control. Alternator output is proportional to load angle, but as the parameter is moved up, the power system security approaches the extreme limit. Hence, generators are operated well below their steady state stability limit for the secure operation of a power system. This raises demand for efficient and fast controllers. Artificial intelligence, specifically artificial neural network (ANN), is emerging very rapidly and has become an efficient tool for operation and control of power systems. ANN requires considerable time to tune weights, but it is fast and accurate once tuned properly. Previously, ANNs have been trained with high-dimensional input space or have been trained online. Hence, either one requires considerable time to yield the control signal or is a bit risky technique to apply in interconnected power systems. In this study, a multilayer perceptron (MLP) ANN is proposed to control generator excitation trained with low-dimensional input space. Moreover, MLP has been trained offline to avert the risk potential of online training. The results illustrate preeminence of the proposed neurocontroller-based excitation system over the conventional controllers-based excitation system.     

Application of neural networks to temperature control in thermal power plants

In a thermal power plant with once-through boilers, it is important to control the temperature at the middle point where water becomes steam. However, there are many problems in the design of such a control system, due to a long system response delay, dead-zone and saturation of the actuator mechanisms, uncertainties in the system model and/or parameters, and process noise. To overcome these problems, an adaptive controller has been designed using neural networks, and tested extensively via simulations.

One of the key problems in designing such a controller is to develop an efficient training algorithm. Neural networks are usually trained using the output errors of the network, instead of using the output errors of the controlled plant. However, when a neural network is used to control a plant directly, the output errors of the network are unknown, since the desired control actions are unknown. This paper proposes a simple training algorithm for a class of nonlinear systems, which enables the neural network to be trained with the output errors of the controlled plant. The only a priori knowledge of the controlled plant is the direction of its output response. Due to its simple structure and algorithm, and good performance, the proposed controller has high potential for handling difficult problems in process-control systems.     

Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.     

Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.     

Intelligent sensor system for discrimination of material type using neural networks

Neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper. The sensor is developed by means of a lightweight plunger probe and an optical mouse sensor. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The experiment is conducted to obtain the bouncing signals for plain surface of an objects kept at different distances from the probe. During the bouncing of the probe, time varying signals are generated from optical mouse that are recorded in data files on PC. Some dominant unique features are then extracted using signal processing tools to optimize neural network based classifier. The time and features of bouncing signal are related to the material type, and each material has a unique set of such properties. It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects). The classifiers are developed using two neural networks configurations, namely a well-known Multi-layer Perceptron Neural Networks (MLP NN), and Radial Basis Function Neural Networks (RBF NN). MLP NN and RBF NN models are designed to maximize accuracy under the constraints of minimum network dimension.The optimal parameters of MLP NN and RBF NN models based on various performance measures that include percentage classification accuracy (PCLA) on the testing data, and area under Receiver Operating Characteristics (ROC), and are determined. For the sensor data set, the PCLA of both the classifiers are found reasonable consistently in respect of rigorous testing using different data partitions. The areas under the ROC curves are close to unity. Performances of the two classifiers have been compared. It has been found that the RBF NN is more robust to noise, and epochs required for training are very less as compared to that for MLP NN.     

基于MLP神经网络的分组密码算法能量分析研究

随着嵌入式密码设备的广泛应用,侧信道分析(side channel analysis,SCA)成为其安全威胁之一。通过对密码算法物理实现过程中的泄露信息进行分析实现密钥恢复,进而对密码算法实现的安全性进行评估。为了精简用于能量分析的多层感知器(multi-layer perceptron,MLP)网络结构,减少模型的训练参数和训练时间,针对基于汉明重量(HW)和基于比特的MLP神经网络的模型进行了研究,输出类别由256分类分别减少为9分类和2分类;通过采集AES密码算法运行过程中的能量曲线对所提出的MLP神经网络进行训练和测试。实验结果表明,该模型在确保预测精度的前提下能减少MLP神经网络84%的训练参数和28%的训练时间,并减少了密钥恢复阶段需要的能量曲线数量,最少只需要一条能量曲线即可完成AES算法完整密钥的恢复。实验验证了模型的有效性,使用该模型可以对分组密码算法实现的安全性进行分析和评估。     

基于OWO-HWO算法的MLP网络在织物染色配色中的应用

讨论了具有非线性、不确定特性的织物染色配色过程建模与仿真问题。针对传统的织物染色配色方法效果差、精确度不高和难以达到期望结果的问题,结合MLP神经网络的特点,提出了基于OWO-HWO算法训练的MLP神经网络,同时分别优化网络输入层到隐层和隐层到输出层的权值,并利用基于OWO-HWO算法的MLP神经网络建立织物染色配色模型。针对此种模型,利用NuMap神经网络软件进行仿真实验。仿真结果表明,该配色模型收敛速度快,精确度高,在解决织物染色配色问题上取得了令人满意的配色效果。     

Sensitivity analysis of multilayer perceptron with differentiableactivation functions

In a neural network, many different sets of connection weights can approximately realize an input-output mapping. The sensitivity of the neural network varies depending on the set of weights. For the selection of weights with lower sensitivity or for estimating output perturbations in the implementation, it is important to measure the sensitivity for the weights. A sensitivity depending on the weight set in a single-output multilayer perceptron (MLP) with differentiable activation functions is proposed. Formulas are derived to compute the sensitivity arising from additive/multiplicative weight perturbations or input perturbations for a specific input pattern. The concept of sensitivity is extended so that it can be applied to any input patterns. A few sensitivity measures for the multiple output MLP are suggested. For the verification of the validity of the proposed sensitivities, computer simulations have been performed, resulting in good agreement between theoretical and simulation outcomes for small weight perturbations.     

用遗传BP网络进行图像边缘检测

该文提出了一种基于遗传算法与图像特征向量的边缘检测方法。由于噪声的干扰,常规的图像边缘检测方法往往效果不佳,因此在充分考虑边缘和噪声本质区别的基础上,构造具有较强抗噪能力的特征向量;然后用样本图像对多层前馈神经网络采用遗传学习算法和误差反向传播算法(BP)相结合进行训练,即先用遗传学习算法进行全局训练,再用BP算法进行精确训练,使网络收敛速度加快和避免局部极小。最后,将训练后的网络用于图像的边缘检测。实验证明这种方法是有效的。