Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 43,21
Anzahl: 4 verfügbar
In den WarenkorbZustand: New. pp. 268 65 Illus.
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 114,33
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: moluna, Greven, Deutschland
EUR 124,02
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. Apart from research efforts bringing together metaheuristic techniques to train artificial neural networks, this is the first book to achieve this objective. This book provides a unified approach to training ANNs with modern heuristics moreover, it prov.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - Metaheuristic Procedures For Training Neural Networks provides successful implementations of metaheuristic methods for neural network training. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Apart from Chapter 1, which reviews classical training methods, the chapters are divided into three main categories. The first one is devoted to local search based methods, including Simulated Annealing, Tabu Search, and Variable Neighborhood Search. The second part of the book presents population based methods, such as Estimation Distribution algorithms, Scatter Search, and Genetic Algorithms. The third part covers other advanced techniques, such as Ant Colony Optimization, Co-evolutionary methods, GRASP, and Memetic algorithms. Overall, the book's objective is engineered to provide a broad coverage of the concepts, methods, and tools of this important area of ANNs within the realm of continuous optimization.