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John McCardle, Neural Network Systems Techniques and Applications . [ABSTRACT FROM AUTHOR]

The article reviews the book "Artificial neural networks: Approximation and learning theory," by Halbert White.

Reviews the book 'Intelligent Engineering Systems Through Artificial Neural Networks. Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Data Mining, and Complex Systems,' vol.10, edited by Cihan H. Dagli, Anna L. Buczak, Joydeep Ghosh, Mark J. Embrechts, Okan Ersoy and Stephen Kercel.

Reviews the book "Naturally Intelligent Systems," by Maureen Caudill and Charles Butler.

Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, these forecasting approaches are limited by the fact that future explanatory variables are unknown. To improve forecasting accuracy, recent hybrid forecasting approaches have developed different feature selection techniques (FSTs) to obtain fewer but more significant explanatory variables. However, these significant explanatory variables will still not be available in the future, despite being screened by effective FSTs. This study proposes the autoregressive integrated moving average (ARIMA) technique to serve as the FST for hybrid forecasting models. Aside from the ARIMA element, the proposed hybrid models also include artificial neural networks (ANN) and multivariate adaptive regression splines (MARS) because of their efficient and fast algorithms and effective forecasting performance. ARIMA can identify significant self-predictor variables that will be available in the future. The significant self-predictor variables obtained can then serve as the inputs for ANN and MARS models. These hybrid approaches have been seldom investigated on the electricity sales forecasting. This study proposes several forecasting models that do not require explanatory variables to forecast the industrial electricity, residential electricity, and commercial electricity sales in Taiwan. The experimental results reveal that the significant self-predictor variables obtained from ARIMA can improve the forecasting accuracy of ANN and MARS models. [ABSTRACT FROM AUTHOR]

The article reviews the book "A Statistical Approach to Neural Networks for Pattern Recognition," by Robert A. Dunne.

The article reviews the book "Complex-Valued Neural Networks," by A. Hirose.

Reviews the book "Independent Component Analysis," by Aapo Hyvarinen, Juha Karhunen and Erkki Oja.

Reviews the book "Neural Networks: An Introductory Guide for Social Scientists," by G. David Garson.