Momentum-based wavelet and double wavelet neural networks for power system applications

In order to minimize the power loss and to control the voltage in the power systems, the proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network are proposed in this paper. The training data are obtained by using linear programming method by solving several abnormal conditions. The control variables considered are generator voltages and transformer taps, and the dependent variables are generator reactive powers and load bus voltages. The IEEE 14-bus system and IEEE 30-bus system are tested using the linear programming, Levenberg–Marquardt artificial neural network, proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network to validate the effectiveness of the proposed MDWNN method. The trained neural networks are capable of controlling the voltage, and reactive power in power systems is proved by the results with the high level of precision and speed.

     

A new class of wavelet networks for nonlinear system identification

A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions.     

A new evolutionary system for evolving artificial neural networks

This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.     

Robust radar target classifier using artificial neural networks

In this paper an artificial neural network (ANN) based radar target classifier is presented, and its performance is compared with that of a conventional minimum distance classifier. Radar returns from realistic aircraft are synthesized using a thin wire time domain electromagnetic code. The time varying backscattered electric field from each target is processed using both a conventional scheme and an ANN-based scheme for classification purposes. It is found that a multilayer feedforward ANN, trained using a backpropagation learning algorithm, provides a higher percentage of successful classification than the conventional scheme. The performance of the ANN is found to be particularly attractive in an environment of low signal-to-noise ratio. The performance of both methods are also compared when a preemphasis filter is used to enhance the contributions from the high frequency poles in the target response.     

A new discretization algorithm based on range coefficient of dispersion and skewness for neural networks classifier

In this paper we propose a new static, global, supervised, incremental and bottom-up discretization algorithm based on coefficient of dispersion and skewness of data range. It automates the discretization process by introducing the number of intervals and stopping criterion. The results obtained using this discretization algorithm show that the discretization scheme generated by the algorithm almost has minimum number of intervals and requires smallest discretization time. The feedforward neural network with conjugate gradient training algorithm is used to compute the accuracy of classification from the data discretized by this algorithm. The efficiency of the proposed algorithm is shown in terms of better discretization scheme and better accuracy of classification by implementing it on six different real data sets.     

Cooperative binary-real coded genetic algorithms for generating and adapting artificial neural networks

This paper describes a genetic system for designing and training feed-forward artificial neural networks to solve any problem presented as a set of training patterns. This system, called GANN, employs two interconnected genetic algorithms that work parallelly to design and train the better neural network that solves the problem. Designing neural architectures is performed by a genetic algorithm that uses a new indirect binary codification of the neural connections based on an algebraic structure defined in the set of all possible architectures that could solve the problem. A crossover operation, known as Hamming crossover, has been designed to obtain better performance when working with this type of codification. Training neural networks is also accomplished by genetic algorithms but, this time, real number codification is employed. To do so, morphological crossover operation has been developed inspired on the mathematical morphology theory. Experimental results are reported from the application of GANN to the breast cancer diagnosis within a complete computer-aided diagnosis system.     

A hybrid system of neural networks and rough sets for road safety performance indicators

Road safety performance indicators are comprehensible tools that provide a better understanding of current safety conditions and can be used to monitor the effect of policy interventions. New insights can be gained in case one road safety index is composed of all risk indicators. The overall safety performance can then be evaluated, and countries ranked. In this paper, a promising structure of neural networks based on decision rules generated by rough sets—is proposed to develop an overall road safety index. This novel hybrid system integrates the ability of neural networks on self-learning and that of rough sets on automatically transforming data into knowledge. By means of simulation, optimal weights are assigned to seven road safety performance indicators. The ranking of 21 European countries in terms of their road safety index scores is compared to a ranking based on the number of road fatalities per million inhabitants. Evaluation results imply the feasibility of this intelligent decision support system and valuable predictive power for the road safety indicators context.     

Focused local learning with wavelet neural networks

A novel objective function is presented that incorporates both local and global errors as well as model parsimony in the construction of wavelet neural networks. Two methods are presented to assist in the minimization of this objective function, especially the local error term. First, during network initialization, a locally adaptive grid is utilized to include candidate wavelet basis functions whose local support addresses the local error of the local feature set. This set can be either user-defined or determined using information derived from the wavelet transform modulus maxima representation. Next, during the network construction, a new selection procedure based on a subspace projection operator is presented to help focus the selection of wavelet basis functions to reduce the local error. Simulation results demonstrate the effectiveness of these methodologies in minimizing local and global error while maintaining model parsimony and incurring a minimal increase on computational complexity.     

用改进的遗传算法训练神经网络构造分类器

针对基本遗传算法存在容易早熟和局部搜索能力弱等缺陷,提出了改进的遗传算法,引入交叉概率和变异概率与个体的适度值相联系,改进了操作算子,而且在交叉操作后又引入模拟退火机制,提高遗传算法的局部搜索能力。同时,用改进的遗传算法和基本的遗传算法训练神经网络构造分类器,实验结果表明,改进的遗传算法在最好个体适度值和最好分类准确性等方面性能更好。     

Searching sets of properties with neural networks

In this paper we develop an algorithm in the framework of neural networks. Specifically we consider the problem of detecting a subset of elements of a set Ω which possess a given property.     

Software development cost estimation using wavelet neural networks

Software development has become an essential investment for many organizations. Software engineering practitioners have become more and more concerned about accurately predicting the cost and quality of software product under development. Accurate estimates are desired but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper, we propose the use of wavelet neural network (WNN) to forecast the software development effort. We used two types of WNN with Morlet function and Gaussian function as transfer function and also proposed threshold acceptance training algorithm for wavelet neural network (TAWNN). The effectiveness of the WNN variants is compared with other techniques such as multilayer perceptron (MLP), radial basis function network (RBFN), multiple linear regression (MLR), dynamic evolving neuro-fuzzy inference system (DENFIS) and support vector machine (SVM) in terms of the error measure which is mean magnitude relative error (MMRE) obtained on Canadian financial (CF) dataset and IBM data processing services (IBMDPS) dataset. Based on the experiments conducted, it is observed that the WNN-Morlet for CF dataset and WNN-Gaussian for IBMDPS outperformed all the other techniques. Also, TAWNN outperformed all other techniques except WNN.     

Imbalance detection and classification system based on wavelet analysis and artificial neural networks

We present a robust algorithm for sequential imbalance detection (detecting a change of properties) for random processes with a wavelet packet transform. Based on this detector and artificial neural networks, we develop a classification system for different types of imbalance. We compare the resulting system with Shewhart control charts. The resulting system can be successfully used in selective control and under other conditions of imbalance detection and classification related to insufficient information about the signal before and after the change.     

Artificial neural networks paddy-field classifier using spatiotemporal remote sensing data

Monitoring changes in a paddy-field area is important since rice is a staple food and paddy agriculture is a major cropping system in Asia. For monitoring changes in land surface, various applications using different satellites have been researched in the field of remote sensing. However, monitoring a paddy-field area with remote sensing is difficult owing to the temporal changes in the land surface, and the differences in the spatiotemporal characteristics in countries and regions. In this article, we used an artificial neural network to classify paddy-field areas using moderate resolution sensor data that includes spatiotemporal information. Our aim is to automatically generate a paddy-field classifier in order to create localized classifiers for each country and region.     

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.     

Classification of parameter changes in a dynamic system with the use of wavelet analysis and neural networks

In this paper a neural detector of internal parameter changes in a stationary, non-linear SISO dynamic system is considered. A dynamic system is usually described by an input-output relation or by a set of state equations. Each change of parameter values creates a new non-nominal model of a dynamic system (sometimes with different values of parameters, sometimes with different structure and different values of parameters). Thus the detection of parameter changes can be formulated as a multi-model classification. The LVQ (Learning Vector Quantisation) neural network has been proposed as a classifier. Selected aggregated properties of discrete wavelet decomposition coefficients of the system output have been chosen as the inputs of the LVQ classifier. The output of the classifier points out the current model. The proposed approach to classification can be adopted as a fault detection method where faults are represented by changes of values of internal parameters of a system. The algorithm has been evaluated on the example of a non-linear fluid system with a non-ideal pipe which internal state is characterised by one value of a parameter, chosen from the known set.     

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.     

YACS: a new learning classifier system using anticipation

 A new and original trend in the learning classifier system (LCS) framework is focussed on latent learning. These new LCSs call upon classifiers with a (condition), an (action) and an (effect) part. In psychology, latent learning is defined as learning without getting any kind of reward. In the LCS framework, this process is in charge of discovering classifiers which are able to anticipate accurately the consequences of actions under some conditions. Accordingly, the latent learning process builds a model of the dynamics of the environment. This model can be used to improve the policy learning process. This paper describes YACS, a new LCS performing latent learning, and compares it with ACS.     

Robust on-line neural learning classifier system for data stream classification tasks

The increasing integration of technology in the different areas of science and industry has resulted in the design of applications that generate large amounts of data on-line. Most often, extracting information from these data is key, in order to gain a better understanding of the processes that the data are describing. Learning from these data poses new challenges to traditional machine learning techniques, which are not typically designed to deal with data in which concepts and noise levels may vary over time. The purpose of this paper is to present supervised neural constructivist system (SNCS), an accuracy-based neural-constructivist learning classifier system that makes use of multilayer perceptrons to learn from data streams with a fast reaction capacity to concept changes. The behavior of SNCS on data stream problems with different characteristics is carefully analyzed and compared with other state-of-the-art techniques in the field. This comparison is also extended to a large collection of real-world problems. The results obtained show that SNCS can function in a variety of problem situations producing accurate classification of data, whether the data are static or in dynamic streams.     

Constructive approximation to real function by wavelet neural networks

We present a type of single-hidden layer feed-forward wavelet neural networks. First, we give a new and quantitative proof of the fact that a single-hidden layer wavelet neural network with n + 1 hidden neurons can interpolate n + 1 distinct samples with zero error. Then, without training, we constructed a wavelet neural network X _a (x, A), which can approximately interpolate, with arbitrary precision, any set of distinct data in one or several dimensions. The given wavelet neural network can uniformly approximate any continuous function of one variable.     

Mental tasks discrimination by neural networks with wavelet transform

The study mainly focuses on the analysis of Electroencephalogram (EEG), to classify mental tasks by using features based on wavelet transform. We have used the daubechies family wavelets, level 6, to transform obtained signal from independent component analyzed EEG signal. As Fourier analysis consists of breaking up a signal into sine waves of various frequencies. Similarly, wavelet analysis is the breaking up of a signal into shifted and scaled versions of the original wavelet. Signals with sharp changes might be better analyzed with a Wavelet than with a Fourier transform. It also makes sense that local features can be described well with wavelets that have local extent. This offers improved features to the neural networks obtaining several classified mental tasks. Through several processes, it led us more developed variety mental tasks classification results. We find that the neural networks perform over 75% success resulting with small number of electrodes better than a previous 70% resulting.