Hybrid neural-evolutionary model for electricity price forecasting

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

Prediction intervals to account for uncertainties in travel time prediction

The accurate prediction of travel times is desirable but frequently prone to error. This is mainly attributable to both the underlying traffic processes and the data that are used to infer travel time. A more meaningful and pragmatic approach is to view travel time prediction as a probabilistic inference and to construct prediction intervals (PIs), which cover the range of probable travel times travelers may encounter. This paper introduces the delta and Bayesian techniques for the construction of PIs. Quantitative measures are developed and applied for a comprehensive assessment of the constructed PIs. These measures simultaneously address two important aspects of PIs: 1) coverage probability and 2) length. The Bayesian and delta methods are used to construct PIs for the neural network (NN) point forecasts of bus and freeway travel time data sets. The obtained results indicate that the delta technique outperforms the Bayesian technique in terms of narrowness of PIs with satisfactory coverage probability. In contrast, PIs constructed using the Bayesian technique are more robust against the NN structure and exhibit excellent coverage probability.

A hybrid neural classifier for dimensionality reduction and data visualization and its application to fault detection and classification of induction motors

In this paper, a hybrid neural classifier combining the auto-encoder neural network and the Lattice Vector Quantization (LVQ) model is described. The auto-encoder network is used for dimensionality reduction by projecting high dimensional data into the 2D space. The LVQ model is used for data visualization by forming and adapting the granularity of a data map. The mapped data are employed to predict the target classes of new data samples. To improve classification accuracy, a majority voting scheme is adopted by the hybrid classifier. To demonstrate the applicability of the hybrid classifier, a series of experiments using simulated and real fault data from induction motors is conducted. The results show that the hybrid classifier is able to outperform the Multi-Layer Perceptron neural network, and to produce very good classification accuracy rates for various fault conditions of induction motors.

An online pruning strategy for supervised ARTMAP-based neural networks

Identifying an appropriate architecture of an artificial neural network (ANN) for a given task is important because learning and generalisation of an ANN is affected by its structure. In this paper, an online pruning strategy is proposed to participate in the learning process of two constructive networks, i.e. fuzzy ARTMAP (FAM) and fuzzy ARTMAP with dynamic decay adjustment (FAMDDA), and the resulting hybrid networks are called FAM/FAMDDA with temporary nodes (i.e. FAM-T and FAMDDA-T, respectively). FAM-T and FAMDDA-T possess a capability of reducing the network complexity online by removing unrepresentative neurons. The performances of FAM-T and FAMDDA-T are evaluated and compared with those of FAM and FAMDDA using a total of 13 benchmark data sets. To demonstrate the applicability of FAM-T and FAMDDA-T, a real fault detection and diagnosis task in a power plant is tested. The results from both benchmark studies and real-world application show that FAMDDA-T and FAM-T are able to yield satisfactory classification performances, with the advantage of having parsimonious network structures.

An experimental study of original and ordered fuzzy ARTMAP neural networks in pattern classification tasks

This paper investigates the effectiveness of an ordering algorithm applied to the supervised Fuzzy ARTMAP (FAM) neural network in pattern classification tasks. Before presenting the input patterns to the FAM network (known as ordered FAM), a fixed order of input patterns is first identified using the ordering algorithm. An experimental study is conducted to compare the results from ordered FAM with the average and voting results from original FAM. In the study, a pool of the original FAM networks is trained using different sequences of input patterns, and the results are averaged. Outputs from various original FAM networks can also be combined using a majority voting strategy to reach a final result. A database comprising various symptoms and measurements of patients suffering from heart attack is used to evaluate the various schemes of the FAM network in medical pattern classification tasks. The results are compared, analyzed, and discussed.

Integration of supervised ART-based neural networks with a hybrid genetic algorithm

In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.

Text-dependent speaker recognition using wavelets and neural networks

An intelligent system for text-dependent speaker recognition is proposed in this paper. The system consists of a wavelet-based module as the feature extractor of speech signals and a neural-network-based module as the signal classifier. The Daubechies wavelet is employed to filter and compress the speech signals. The fuzzy ARTMAP (FAM) neural network is used to classify the processed signals. A series of experiments on text-dependent gender and speaker recognition are conducted to assess the effectiveness of the proposed system using a collection of vowel signals from 100 speakers. A variety of operating strategies for improving the FAM performance are examined and compared. The experimental results are analyzed and discussed.

Accurate modeling of low actuation voltage RFMEMS switches using artificial neural networks

This paper presents a fast and accurate method for extracting the scattering parameters of a RF MEMS switch by using its essential parameters. A neural network is developed for parametric modeling of the switch. The essential parameters of the switch are analyzed in terms of its return loss and isolation with variation of its geometrical component values. Simulation results show that the proposed approach can be used to accurately model the RF characteristics of RF-MEMS switches. The results show good agreement between the neural network prediction and electromagnetic simulations.

Development of a speaker recognition system using wavelets and artificial neural networks

This paper addresses the problem of speaker recognition from speech signals. The study focuses on the development of a speaker recognition system comprising two modules: a wavelet-based feature extractor, and a neural-network-based classifier. We have conducted a number of experiments to investigate the applicability of Discrete Wavelet Transform (D WT) in extracting discriminative features from the speech signals, and have examined various models from the Adaptive Resonance Theory (ART) family of neural networks in classijjing the extracted features. The results indicate that DWT could be a potential feature extraction tool for speaker recognition. In addition, the ART-based classijiers have yielded very promising recognition accuracy at more than 81%.

Prediction intervals for electricity load forecasting using neural networks

Most of the research in time series is concerned with point forecasting. In this paper we focus on interval forecasting and its application for electricity load prediction. We extend the LUBE method, a neural network-based method for computing prediction intervals. The extended method, called LUBEX, includes an advanced feature selector and an ensemble of neural networks. Its performance is evaluated using Australian electricity load data for one year. The results showed that LUBEX is able to generate high quality prediction intervals, using a very small number of previous lag variables and having acceptable training time requirements. The use of ensemble is shown to be critical for the accuracy of the results.

A review of online learning in supervised neural networks

Learning in neural networks can broadly be divided into two categories, viz., off-line (or batch) learning and online (or incremental) learning. In this paper, a review of a variety of supervised neural networks with online learning capabilities is presented. Specifically, we focus on articles published in main indexed journals in the past 10 years (2003–2013). We examine a number of key neural network architectures, which include feedforward neural networks, recurrent neural networks, fuzzy neural networks, and other related networks. How the online learning methodologies are incorporated into these networks is exemplified, and how they are applied to solving problems in different domains is highlighted. A summary of the review that covers different network architectures and their applications is presented.

Use of artificial neural networks as explicit finite difference operators

Results of a numerical exercise, substituting a numerical operator by an artificial neural network (ANN) are presented in this paper. The numerical operator used is the explicit form of the finite difference (FD) scheme. The FD scheme was used to discretize the one-dimensional transport equation, which included both the advection and dispersion terms. Inputs to the ANN are the FD representation of the transport equation, and the concentration was designated as the output. Concentration values used for training the ANN were obtained from analytical solutions. The numerical operator was reconstructed from a back calculation of the weights of the ANN. Linear transfer functions were used for this purpose. The ANN was able to accurately recover the velocity used in the training data, but not the dispersion coefficient. This capability was improved when numerical dispersion was taken into account; however, it is limited to the condition: C/P<0.5 , where C is the Courant number and P , the Peclet number (i.e., the restriction imposed by the Neumann stability condition).

Hybrid neural network—finite element river flow model

Results obtained from a hybrid neural network—finite element model are reported in this paper. The hybrid model incorporates artificial neural network (ANN) nodes into a numerical scheme, which solves the two-dimensional shallow water equations using finite elements (FE). First, numerical computations are carried out on the entire numerical model, using a larger mesh. The results from this computation are then used to train several preselected ANN nodes. The ANN nodes model the response for a part of the entire numerical model by transferring the system reaction to the location where both models are connected in real time. This allows a smaller mesh to be used in the hybrid ANN-FE model, resulting in savings in computation time. The hybrid model was developed for a river application, using the computational nodes located at the open boundaries to be the ANN nodes for the ANN-FE hybrid model. Real-time coupling between the ANN and FE models was achieved, and a reduction is CPU time of more than 25% was obtained.