Nonlinear system identification using optimized dynamic neural network

In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme—Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.     

Nonlinear system identification using a Gabor/Hopfield network

This paper presents a method of nonlinear system identification using a new Gabor/Hopfield network. The network can identify nonlinear discrete-time models that are affine linear in the control. The system need not be asymptotically stable but must be bounded-input-bounded-output (BIBO) stable for the identification results to be valid in a large input-output range. The network is a considerable improvement over earlier work using Gabor basis functions (GBF's) with a back-propagation neural network. Properties of the Gabor model and guidelines for achieving a global error minimum are derived. The new network and its use in system identification are investigated through computer simulation. Practical problems such as local minima, the effects of input and initial conditions, the model sensitivity to noise, the sensitivity of the mean square error (MSE) to the number of basis functions and the order of approximation, and the choice of forcing function for training data generation are considered.     

Fingerprint classification using a fuzzy multilayer perceptron

A fuzzy multilayer perceptron is used for the classification of fingerprint patterns. The input vector consists of texturebased features along with some directional features. The output vector is defined in terms of membership values to the three classes, viz.Whorl, Left Loop and Right Loop. Perturbation is produced randomly at pixel locations to generate noisy patterns. This helps to demonstrate the ability of the model in handling distorted fingerprint images. A study is made on the effect of reducing the number of input features while increasing the size of the network on its recognition performance.     

Human face recognition using fuzzy multilayer perceptron

In this work a novel method for human face recognition that is based on fuzzy neural network has been presented. Here, Gabor wavelet transformation is used for extraction of features from face images as it deals with images in spatial as well as in frequency domain to capture different local orientations and scales efficiently. In face recognition problem multilayer perceptron (MLP) has already been adopted owing to its efficiency, but it does not capture overlapping and nonlinear manifolds of faces which exhibit different variations in illumination, expression, pose, etc. A fuzzy MLP on the other hand performs better than an MLP because fuzzy MLP can identify decision surfaces in case of nonlinear overlapping classes, whereas an MLP is restricted to crisp boundaries only. In the present work, a new approach for fuzzification of the feature sets obtained through Gabor wavelet transforms has been discussed. The feature vectors thus obtained are classified using a newly designed fuzzified MLP. The system has been tested on a composite database (DB-C) consisting of the ORL face database and another face database created for this purpose and a recognition rate of 97.875% with fuzzy MLP against a recognition rate of only 81.25% with MLP whose feature vectors were also obtained through same Gabor wavelet transforms has been obtained.     

基于递归神经网络的非平稳网络丢包层析成像

为了提高互联网的管理和控制水平.进而优化配置网络资源,一种新的估计网络内部参数的方法"网络层析成像"得到了广泛关注.提出一种基于递归神经网络的非平稳网络丢包层析成像方法,利用递归多层感知器求解非平稳网络丢包模型.采用NS2仿真工具进行实验,证明了该算法能够自适应非平稳网络丢包率随时间变化而产生的波动,以实时追踪网络内部链路的丢包率.     

Conventional modeling of the multilayer perceptron using polynomialbasis functions

A technique for modeling the multilayer perceptron (MLP) neural network, in which input and hidden units are represented by polynomial basis functions (PBFs), is presented. The MLP output is expressed as a linear combination of the PBFs and can therefore be expressed as a polynomial function of its inputs. Thus, the MLP is isomorphic to conventional polynomial discriminant classifiers or Volterra filters. The modeling technique was successfully applied to several trained MLP networks.     

An on-line hybrid learning algorithm for multilayer perceptron in identification problems

A hybrid learning algorithm for multilayered perceptrons (MLPs) and pattern-by-pattern training, based on optimized instantaneous learning rates and the recursive least squares method, is proposed. This hybrid solution is developed for on-line identification of process models based on the use of MLPs, and can speed up the learning process of the MLPs substantially, while simultaneously preserving the stability of the learning process. For illustration and test purposes the proposed algorithm is applied to the identification of a non-linear dynamic system.     

Nonlinear dynamic system identification using pipelined functional link artificial recurrent neural network

A computationally efficient pipelined functional link artificial recurrent neural network (PFLARNN) is proposed for nonlinear dynamic system identification using a modification real-time recurrent learning (RTRL) algorithm in this paper. In contrast to a feedforward artificial neural network (such as a functional link artificial neural network (FLANN)), the proposed PFLARNN consists of a number of simple small-scale functional link artificial recurrent neural network (FLARNN) modules. Since those modules of PFLARNN can be performed simultaneously in a pipelined parallelism fashion, this would result in a significant improvement in its total computational efficiency. Moreover, nonlinearity of each module is introduced by enhancing the input pattern with nonlinear functional expansion. Therefore, the performance of the proposed filter can be further improved. Computer simulations demonstrate that with proper choice of functional expansion in the PFLARNN, this filter performs better than the FLANN and multilayer perceptron (MLP) for nonlinear dynamic system identification.     

Training multilayer perceptron classifiers based on a modifiedsupport vector method

In this paper we describe a training method for one hidden layer multilayer perceptron classifier which is based on the idea of support vector machines (SVM). An upper bound on the Vapnik-Chervonenkis (VC) dimension is iteratively minimized over the interconnection matrix of the hidden layer and its bias vector. The output weights are determined according to the support vector method, but without making use of the classifier form which is related to Mercer's condition. The method is illustrated on a two-spiral classification problem.     

Nonlinear system identification using wavelet networks

A novel identification scheme using wavelet networks is presented for nonlinear dynamical systems. Based on fixed wavelet networks, parameter adaptation laws are developed using a Lyapunov synthesis approach. This guarantees the stability of the overall identification scheme and the convergence of both the parameters and the state errors, even in the presence of modelling errors. Using the decomposition and reconstruction techniques of multiresolution decompositions, variable wavelet networks are introduced to achieve a desired estimation accuracy and a suitable sized network, and to adapt to variations of the characteristics and operating points in nonlinear systems. B-spline wavelets are used to form the wavelet networks and the identification scheme is illustrated using a simulated example.     

High-order and multilayer perceptron initialization

Proper initialization is one of the most important prerequisites for fast convergence of feedforward neural networks like high-order and multilayer perceptrons. This publication aims at determining the optimal variance (or range) for the initial weights and biases, which is the principal parameter of random initialization methods for both types of neural networks. An overview of random weight initialization methods for multilayer perceptrons is presented. These methods are extensively tested using eight real-world benchmark data sets and a broad range of initial weight variances by means of more than 30000 simulations, in the aim to find the best weight initialization method for multilayer perceptrons. For high-order networks, a large number of experiments (more than 200000 simulations) was performed, using three weight distributions, three activation functions, several network orders, and the same eight data sets. The results of these experiments are compared to weight initialization techniques for multilayer perceptrons, which leads to the proposal of a suitable initialization method for high-order perceptrons. The conclusions on the initialization methods for both types of networks are justified by sufficiently small confidence intervals of the mean convergence times.     

Multimedia-oriented action recognition in Smart City-based IoT using multilayer perceptron

The Internet of Things (IoT) devices and technologies for smart city applications produces a vast amount of multimedia data (e.g., audio, video, image, text and sensorial data), such big data are difficult to handle with traditional techniques and algorithms. The emerging machine learning techniques have the potential to facilitate the development of a new class of applications that can deal with such multimedia big data. Recently, Activity Recognition systems suggest using of multimedia data to detect daily actions, since it provides more accurate patterns; prevent the arising complain on privacy issues (in case of using audio-base data) and able to work on a big data. In this paper, we propose a Deep Learning (DL) methodology for classifying audio data that is based on multilayer perceptron neural networks. The contributions of our work are to propose an efficient design of the network topology including hidden layers, neurons, and the fitness function. In addition, the proposed methodology contributed in producing high performance classifier in terms of accuracy and f-measure. The experiments have been conducted on four large audio-datasets that have been collected to represent different modalities in a smart city. The results indicated that the proposed methodology achieved high performance as compared to the state-of-the-art machine learning techniques.

     

具有多层感知器力矩补偿的机器人自抗扰控制

本文针对机器人系统的控制特性,提出了一种基于自抗扰控制(ADRC)的关节控制算法,该算法可以克服传统控制算法中存在的如系统抗干扰能力弱,控制性能受限于建模精度,动态性能与稳态性能难以兼顾,控制律设计较为复杂等问题.针对受控系统特性给出了一套实际控制器的完整设计方法与参数整定方法,并根据控制性能指标设计优化函数完成了最优控制参数的优化,在系统参数辨识的基础上利用多层感知器(MLP)设计了对建模不确定性的补偿网络.数值仿真和实验结果均表明该算法能够实现机器人快速稳定的轨迹跟踪,具有良好的控制精度与很强的抗干扰能力,此外该算法不依赖于精确的系统模型,降低了实际设计和应用的难度,具有很好的工程应用价值.     

Evaluation of an optimal design method for a multilayer perceptron by using the design of experiments

We evaluated the performance of an optimal design method for a multilayer perceptron (MLP) by using the design of experiments (DOE). In our previous work, we proposed an optimal design method for MLPs in order to determine the optimal values of such parameters as the number of neurons in the hidden layers and the learning rates. In this article, we evaluate the performance of the proposed design method through a comparison with a genetic algorithm (GA)-based design method. We target an optimal design of MLPs with six layers. We also evaluate the proposed designed method in terms of calculating the amount of optimization. Through the above-mentioned evaluation and analysis, we aim at improving the proposed design method in order to obtain an optimal MLP with less effort.     

Clustered defect detection of high quality chips using self-supervised multilayer perceptron

During electrical testing, each die on a wafer must be tested to determine whether it functions as originally designed. When defects, including scratches, stains or localized failed patterns, are clustered on the wafer, the tester may not detect all of the defective dies in the flawed area. A testing factory must assign a few workers to check the wafers and hand-mark the defective dies in the flawed region or close to the flawed region, to ensure that no defective die is present in the final assembly. This work presents an automatic wafer-scale defect cluster identifier that uses a multilayer perceptron to detect the defect cluster and mark all of the defective dies. The proposed identifier is compared with an existing tool used in industry. The experimental results confirm that the proposed algorithm is more effective at identifying defects and outperforms the present approach.     

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as backpropagation and can also be used to provide insight into the learning process and the nature of the error surface.     

Predicting the helpfulness of online reviews using multilayer perceptron neural networks

With the great development of e-commerce, users can create and publish a wealth of product information through electronic communities. It is difficult, however, for manufacturers to discover the best reviews and to determine the true underlying quality of a product due to the sheer volume of reviews available for a single product. The goal of this paper is to develop models for predicting the helpfulness of reviews, providing a tool that finds the most helpful reviews of a given product. This study intends to propose HPNN (a helpfulness prediction model using a neural network), which uses a back-propagation multilayer perceptron neural network (BPN) model to predict the level of review helpfulness using the determinants of product data, the review characteristics, and the textual characteristics of reviews. The prediction accuracy of HPNN was better than that of a linear regression analysis in terms of the mean-squared error. HPNN can suggest better determinants which have a greater effect on the degree of helpfulness. The results of this study will identify helpful online reviews and will effectively assist in the design of review sites.     

On the classification of multispectral satellite images using the multilayer perceptron

For classifying multispectral satellite images, a multilayer perceptron (MLP) is trained using either (i) ground truth data or (ii) the output of a K-means clustering program or (iii) both, as applied to certain representative parts of the given data set. In the second case, different sets of clustered image outputs, which have been checked against actual ground truth data wherever available, are used for testing the MLP. The cover classes are, typically, different types of (a) vegetation (including forests and agriculture); (b) soil (including mountains, highways and rocky terrain); and (c) water bodies (including lakes). Since the extent of ground truth may not be sufficient for training neural networks, the proposed procedure (of using clustered output images) is believed to be novel and advantageous. Moreover, it is found that the MLP offers an accuracy of more than 99% when applied to the multispectral satellite images in our library. As importantly, comparison with some recent results shows that the proposed application of the MLP leads to a more accurate and faster classification of multispectral image data.     

Nonlinear system identification with continuous piecewise linear neural network

This paper considers system identification using domain partition based continuous piecewise linear neural network (DP-CPLNN), which is newly proposed. DP-CPLNN has the capability of representing any continuous piecewise linear (CPWL) function, hence its identification performance can be expected. Another attractive feature of DP-CPLNN is the geometrical property of its parameters. Applying this property, this paper proposes an identification method including domain partition and parameter training. In numerical experiments, DP-CPLNN with this method outperforms hinging hyperplanes and high-level canonical piecewise linear representation, which are two widely used CPWL models, showing the flexibility of DP-CPLNN and the effectiveness of the proposed algorithm in nonlinear identification.     

A perceptron network for functional identification and control ofnonlinear systems

Tracking control of a general class of nonlinear systems using a perceptron neural network (PNN) is presented. The basic structure of the PNN and its training law are first derived. A novel discrete-time control strategy is introduced that employs the PNN for direct online estimation of the required feedforward control input. The developed controller can be applied to both discrete- and continuous-time plants. Unlike most of the existing direct adaptive or learning schemes, the nonlinear plant is not assumed to be feedback linearizable. The stability of the neural controller under ideal conditions and its robust stability to inexact modeling information are rigorously analyzed.