Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.     

Hybrid neural network models for hydrologic time series forecasting

The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.     

Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction

Traditional parametric software reliability growth models (SRGMs) are based on some assumptions or distributions and none such single model can produce accurate prediction results in all circumstances. Non-parametric models like the artificial neural network (ANN) based models can predict software reliability based on only fault history data without any assumptions. In this paper, initially we propose a robust feedforward neural network (FFNN) based dynamic weighted combination model (PFFNNDWCM) for software reliability prediction. Four well-known traditional SRGMs are combined based on the dynamically evaluated weights determined by the learning algorithm of the proposed FFNN. Based on this proposed FFNN architecture, we also propose a robust recurrent neural network (RNN) based dynamic weighted combination model (PRNNDWCM) to predict the software reliability more justifiably. A real-coded genetic algorithm (GA) is proposed to train the ANNs. Predictability of the proposed models are compared with the existing ANN based software reliability models through three real software failure data sets. We also compare the performances of the proposed models with the models that can be developed by combining three or two of the four SRGMs. Comparative studies demonstrate that the PFFNNDWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models. Numerical and graphical explanations show that PRNNDWCM is promising for software reliability prediction since its fitting and prediction error is much less relative to the PFFNNDWCM.     

一种网络流量预测的小波神经网络模型

结合小波变换和人工神经网络的优势,建立一种网络流量预测的小波神经网络模型。首先对流量时间序列进行小波分解,得到小波变换尺度系数序列和小波系数序列,以系数序列和原来的流量时间序列分别作为模型的输入和输出,构造人工神经网络并且加以训练。用实际网络流量对该模型进行验证,结果表明,该模型具有较高的预测效果。     

Intelligent transportation control system design using wavelet neural network and PID-type learning algorithms

In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) and proportional-integral-derivative-type (PID-type) learning algorithms is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and an auxiliary compensation controller. The neural controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of an ideal total sliding-mode control (TSMC) law. The PID-type learning algorithms are derived from the Lyapunov stability theorem, which are utilized to adjust the parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H_∞ control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved. Finally, to investigate the effectiveness of the proposed control strategy, it is applied to control a marine transportation system and a land transportation system. The simulation results demonstrate that the proposed WNN-based ITCS with PID-type learning algorithms can achieve favorable control performance than other control methods.     

Prediction of programmed-temperature retention values of naphthas by wavelet neural networks

The wavelet neural network (WNN) was used to predict the programmed-temperature retention values of naphthas. In WNN, a Morlet mother wavelet was used as a transfer function, and the convergence speed was faster than other neural networks. Sixty-four compounds (selected randomly from 94) were used as a training set, and the 30 remaining compounds were used as a test set. A very satisfactory result was obtained only after about 8000 training epochs. The other two methods, the artificial neural network (ANN) and the Simpson integral method, were also used for this study. The comparison of results obtained from three methods showed that the WNN is the most suitable tool in predicting programmed-temperature retention values of naphthas, consequently this method can be used to provide reliable data for the petrochemical industry.     

基于小波神经网络的木材干燥窑内传感器建模研究

针对木材干燥系统具有非线性、强耦合的特性,难以建立准确的数学模型,提出一种基于小波神经网络的建模方法。通过木材干燥窑内木材含水率传感器、温度传感器和湿度传感器采集的数据建立小波神经网络模型,并通过模型预测木材含水率传感器的测量值。小波神经网络将BP神经网络在非线性问题上自学习的能力与小波表征信号局部信息的能力相结合,具有很强的自适应分辨性和容错能力。利用实际木材干燥过程中采集的数据作为训练样本进行仿真实验。结果表明：小波神经网络方法建立的模型能够预测木材含水率传感器的测量值,模型泛化能力强,预测精度高于BP神经网络建立的模型,验证了小波神经网络对木材干燥窑内传感器建模的可行性和有效性。     

Prediction of power output of a coal-fired power plant by artificial neural network

Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported.     

Efficient prediction of exchange rates with low complexity artificial neural network models

In recent years forecasting of financial data such as interest rate, exchange rate, stock market and bankruptcy has been observed to be a potential field of research due to its importance in financial and managerial decision making. Survey of existing literature reveals that there is a need to develop efficient forecasting models involving less computational load and fast forecasting capability. The present paper aims to fulfill this objective by developing two novel ANN models involving nonlinear inputs and simple ANN structure with one or two neurons. These are: functional link artificial neural network (FLANN) and cascaded functional link artificial neural network (CFLANN). These have been employed to predict currency exchange rate between US$ to British Pound, Indian Rupees and Japanese Yen. The performance of the proposed models have been evaluated through simulation and have been compared with those obtained from standard LMS based forecasting model. It is observed that the CFLANN model performs the best followed by the FLANN and the LMS models.     

An adaptive wavelet network for function learning

In this article, a wavelet neural network (WNN) model is proposed for approximating arbitrary nonlinear functions. Our WNN model structure comes from the idea of adaptive neuro-fuzzy inference system (ANFIS) which is used for obtaining fuzzy rule base from the input–output data of an unknown function. The WNN model which is called in this study as adaptive wavelet network (AWN) consists of wavelet scaling functions in its processing units whereas in an ANFIS, mostly Gaussian-type membership functions are used for a function approximation. We present to train an AWN by a hybrid-learning method containing least square estimation (LSE) with gradient-based optimization algorithm to obtain the optimal translation and dilation parameters of our AWN for model accuracy. Simulation examples are also given to illustrate the effectiveness of the method.     

Artificial neural network modelling of the bioethanol-to-olefins process on a HZSM-5 catalyst treated with alkali

In this work the kinetic modelling of the transformation of bioethanol-to-olefins (BTO) process over a HZSM-5 catalyst treated with alkali using artificial neural networks (ANN) is presented. The main goal has been to obtain a BTO process neuronal model with the desired accuracy that allows the simplification and reduction of the computational cost with respect to a mechanistic knowledge model. To check the goodness of ANN base model structures, during the study a comparison with other alternative modelling techniques such as support vector machines was performed. Following a parameters optimization procedure and testing different training methods, the optimal ANN structure results to be a feed-forward 3–5–1 network with the Bayesian regularization training method. Using a set of experimental data obtained in a laboratory scale fixed bed reactor, we have obtained a similar fit to the knowledge model but with the advantage of being up to 43 times faster. These results are important for moving forward real time automatic control strategies in the biorefinery context.     

小波神经网络模型的改进方法

为了改善小波神经网络(WNN)在处理复杂非线性问题的性能,针对量子粒子群优化(QPSO)算法易早熟、后期多样性差、搜索精度不高的缺点,提出一种同时引入加权系数、引入Cauchy随机数、改进收缩扩张系数和引入自然选择的改进量子粒子群优化算法,将其代替梯度下降法,训练小波基系数和网络权值,再将优化后的参数组合输入小波神经网络,以实现算法的耦合。通过对3个UCI标准数据集的仿真实验表明,与WNN、PSO-WNN、QPSO-WNN算法相比,改进的量子粒子群小波神经网络(MQPSO-WNN)算法的运行时间减少了11%~43%,而计算相对误差较之降低了8%~57%。因此,改进的量子粒子群小波神经网络模型能够更迅速、更精确地逼近最优值。     

Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

A new load forecasting (LF) approach using bacterial foraging technique (BFT) trained wavelet neural network (WNN) is proposed in this paper. Artificial neural network (ANN) is combined with wavelet transform called wavelet neural network is applied for LF. The parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned using BFT optimization. With the advantages of global search abilities of BFT as well as the multiresolution and localizing natures of wavelets, the networks are constructed which identifies the inherent non-linear characteristics of power system loads. The proposed approach is validated with Tamil Nadu Electricity Board (TNEB) system, India. The comparison of Delta Rule and BFT-based LF for different periods are depicted with their mean absolute percentage errors (MAPE).     

小波神经网络在滚动轴承故障诊断中的应用

结合小波变换和神经网络的优势给出小波神经网络的结构模型,研究了小波神经网络的学习算法;针对传统算法收敛速度慢等问题,从学习率和引入动量项两个方面对算法进行改进。应用小波网络对滚动轴承的典型故障进行实例诊断。以7216圆锥轴承在实验台上所测取的数据进行网络训练。用振动信号为网络输入向量,给出训练结果。仿真实例表明,采用小波神经网络能够很好地对故障进行分类,其收敛速度明显要快于相同条件BP神经网络,有效地实现了滚动轴承的故障诊断。     

改进利益驱动神经网络在欺诈信息的应用研究

为了研究个体收益和代价实现总体净收益的最大化问题,提出了利益驱动的人工神经网络（ANN）分类方法。该方法引入了惩罚函数,根据实例不同的重要程度对不同实例的误分类给予可变惩罚,并在之后对净利益进行最大化处理。为了生成对个体的惩罚,参照每个实例的收益,通过改变函数值对误差平方和函数进行了修改,提出了七个不同版本的ANN模型。两个欺诈信息的实验结果表明,与原ANN、决策树和朴素贝叶斯分类器相比,所提模型的不同版本在净利润项上的性能优于其他方法,而且能够针对不同的数据集采用不同的权值生成方式。     

Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir

In this work we investigate how artificial neural network (ANN) evolution with genetic algorithm (GA) improves the reliability and predictability of artificial neural network. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Our methodology utilizes a hybrid genetic algorithm–neural network strategy (GA–ANN). The proposed algorithm combines the local searching ability of the gradient–based back-propagation (BP) strategy with the global searching ability of genetic algorithms. Genetic algorithms are used to decide the initial weights of the gradient decent methods so that all the initial weights can be searched intelligently. The genetic operators and parameters are carefully designed and set avoiding premature convergence and permutation problems. For an evaluation purpose, the performance and generalization capabilities of GA–ANN are compared with those of models developed with the common technique of BP. The results demonstrate that carefully designed genetic algorithm-based neural network outperforms the gradient descent-based neural network.     

Intelligent system based on local linear wavelet neural network and recursive least square approach for breast cancer classification

A new learning technique for local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of Error Back propagation and Recursive Least Square (RLS) is introduced for training the parameters of LLWNN. The variance and centers of LLWNN are updated using back propagation and weights are updated using Recursive Least Square (RLS). Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification.     

基于小波神经网络建立虚拟仪器非线性软校正模型

神经网络具有良好的学习特性,小波变换有良好的时频局部化性质,将二者结合在一起构成小波神经网络兼有神经网络和小波变换的优点。本文提出了解决虚拟仪器系统非线性校正问题的小波神经网络算法。最后通过一个应用实例表明,采用小波神经网络建立软校正模型,不仅可以使系统获得高精度,而且在相同的误差条件下,其收敛速度也要远远快于传统的BP神经网络。     

Regression-based weight generation algorithm in neural network for solution of initial and boundary value problems

This paper introduces a new algorithm for solving ordinary differential equations (ODEs) with initial or boundary conditions. In our proposed method, the trial solution of differential equation has been used in the regression-based neural network (RBNN) model for single input and single output system. The artificial neural network (ANN) trial solution of ODE is written as sum of two terms, first one satisfies initial/boundary conditions and contains no adjustable parameters. The second part involves a RBNN model containing adjustable parameters. Network has been trained using the initial weights generated by the coefficients of regression fitting. We have used feed-forward neural network and error back propagation algorithm for minimizing error function. Proposed model has been tested for first, second and fourth-order ODEs. We also compare the results of proposed algorithm with the traditional ANN algorithm. The idea may very well be extended to other complicated differential equations.