Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.

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This book addresses the issues, intricacies, and challenges relating to energy and environmental sciences. Papers cover various technological aspects of sustainable energy eco-systems and processes that improve energy efficiency, and reduce and sequestrate carbon dioxide (CO₂) and other greenhouse emissions. The collection also emphasizes the need for sustainable technologies in extractive metallurgy, materials processing and manufacturing industries with reduced energy consumption and CO₂ emission. Industrial energy efficient technologies include innovative ore...