This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers,and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference.- Recurrent methods- Boltzmann machines- Constructive learning with methods for the reduction of complexity in neural network systems- Modular systems- Associative memory- Neural network design based on the concept of the Inductive Logic Unit- Data classification- Integrated neuron model systems that function as programmable rational approximators

This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers,and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference.- Recurrent methods- Boltzmann machines- Constructive learning with methods for the reduction of complexity in neural network systems- Modular systems- Associative memory- Neural network design based on the concept of the Inductive Logic Unit- Data classification- Integrated neuron model systems that function as programmable rational approximators