Tommy W. S. Chow

Neural network based short-term load forecasting using weather compensation

This paper presents a novel technique for electric load forecasting based on neural weather compensation. Our proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. Our weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.

A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics

In this paper, the authors present a real-time learning control scheme for unknown nonlinear dynamical systems using recurrent neural networks (RNNs). Two RNNs, based on the same network architecture, are utilized in the learning control system. One is used to approximate the nonlinear system, and the other is used to mimic the desired system response output. The learning rule is achieved by combining the two RNNs to form the neural network control system. A generalized real-time iterative learning algorithm is developed and used to train the RNNs. The algorithm is derived by means of two-dimensional (2-D) system theory that is different from the conventional algorithms that employ the steepest optimization to minimize a cost function. This paper shows that an RNN using the real-time iterative learning algorithm can approximate any trajectory tracking to a very high degree of accuracy. The proposed learning control scheme is applied to numerical problems, and simulation results are included. The results are very promising, and this paper suggests that the 2-D system theory-based RNN learning algorithm provides a new dimension in real-time neural control systems.

Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis

Bearings are critical components in induction motors and brushless direct current motors. Bearing failure is the most common failure mode in these motors. By implementing health monitoring and fault diagnosis of bearings, unscheduled maintenance and economic losses caused by bearing failures can be avoided. This paper introduces trace ratio linear discriminant analysis (TR-LDA) to deal with high-dimensional non-Gaussian fault data for dimension reduction and fault classification. Motor bearing data with single-point faults and generalized-roughness faults are used to validate the effectiveness of the proposed method for fault diagnosis. Comparisons with other conventional methods, such as principal component analysis, local preserving projection, canonical correction analysis, maximum margin criterion, LDA, and marginal Fisher analysis, show the superiority of TR-LDA in fault diagnosis.

A neural-based crowd estimation by hybrid global learning algorithm

A neural-based crowd estimation system for surveillance in complex scenes at underground station platform is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Those feature indexes are modeled by a neural network to estimate the crowd density. The learning phase is based on our proposed hybrid of the least-squares and global search algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results are obtained in terms of accuracy and real-time response capability to alert operators automatically.

Induction machine fault diagnostic analysis with wavelet technique

A wavelet transform based method was developed for diagnosing machine faults operating at different rotating speeds. This paper shows that machine fault diagnosis can be effectively performed when an appropriate narrow-band filter is used to extract the required spectra components. A wavelets-transform-based technique is used to design specified narrow filter banks. This enables effective machine fault diagnostic analysis to be performed in the frequency domain. Gaussian-enveloped oscillation-type wavelet is employed. By matching the wavelet basis functions with the associated faulty signals, the required narrow filter banks are obtained. As a result, the detection and diagnosis of machine faults operating at different rotating speeds are made possible. The proposed technique was thoroughly tested at different rotating speeds.

Induction machine fault detection using SOM-based RBF neural networks

A radial-basis-function (RBF) neural-network-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an RBF-type neural network for fault identification and classification. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting grid algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.

Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density

The self-organizing map (SOM) has been widely used in many industrial applications. Classical clustering methods based on the SOM often fail to deliver satisfactory results, specially when clusters have arbitrary shapes. In this paper, through some preprocessing techniques for filtering out noises and outliers, we propose a new two-level SOM-based clustering algorithm using a clustering validity index based on inter-cluster and intra-cluster density. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data better than the classical clustering algorithms based on the SOM, and find an optimal number of clusters.

Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information

A novel feature selection method using the concept of mutual information (MI) is proposed in this paper. In all MI based feature selection methods, effective and efficient estimation of high-dimensional MI is crucial. In this paper, a pruned Parzen window estimator and the quadratic mutual information (QMI) are combined to address this problem. The results show that the proposed approach can estimate the MI in an effective and efficient way. With this contribution, a novel feature selection method is developed to identify the salient features one by one. Also, the appropriate feature subsets for classification can be reliably estimated. The proposed methodology is thoroughly tested in four different classification applications in which the number of features ranged from less than 10 to over 15000. The presented results are very promising and corroborate the contribution of the proposed feature selection methodology.

A weight initialization method for improving training speed in feedforward neural network

Abstract An algorithm for determining the optimal initial weights of feedforward neural networks based on the Cauchys inequality and a linear algebraic method is developed. The algorithm is computational efficient. The proposed method ensures that the outputs of neurons are in the active region and increases the rate of convergence. With the optimal initial weights determined, the initial error is substantially smaller and the number of iterations required to achieve the error criterion is significantly reduced. Extensive tests were performed to compare the proposed algorithm with other algorithms. In the case of the sunspots prediction, the number of iterations required for the network initialized with the proposed method was only 3.03% of those started with the next best weight initialization algorithm.

Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach

A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth movers distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases.