Описание: The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component.
Автор: Graupe Daniel Название: Principles Of Artificial Neural Networks (3Rd Edition) ISBN: 9814522732 ISBN-13(EAN): 9789814522731 Издательство: World Scientific Publishing Рейтинг: Цена: 19008.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition - all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained.The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.
Описание: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Автор: Subana Shanmuganathan; Sandhya Samarasinghe Название: Artificial Neural Network Modelling ISBN: 3319284932 ISBN-13(EAN): 9783319284934 Издательство: Springer Рейтинг: Цена: 19591.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Artificial Neural Networks Applications: An Introduction.- Order in the Black Box: Consistency And Robustness Of Neuron Activation Of Feed Forward Neural Networks And Its Use In Efficient Optimization Of Network Structure.- Artificial Neural Networks as Models of Robustness in Development And Regeneration: Stability of Memory During Morphological Remodeling.- A Structure Optimization Algorithm of Neural Networks For Pattern Learning from Educational Data.- Stochastic Neural Networks for Modelling Random Processes from Observed Data.- Curvelet Interaction with Artificial Neural Networks.- Hybrid Wavelet Neural Network Approaches.-Quantification of Prediction Uncertainty in Artificial Neural Network Models.- Classifying Calpain Inhibitors for The Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery.- Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Using Neural Networks Incorporating a Novel Effective Feature - Degree of Central Regularity of Mass.-SOM Clustering and Modelling of Australian Railway Drivers' Sleep, Wake, Duty Profiles.- A Neural Approach to Electricity Demand Forecasting.- Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia.- Artificial Neural Networks in Precipitation Nowcasting: An Australian Case Study.- Construction of Pmx Concentration Surfaces Using Neural Evolutionary Fuzzy Models of Semi Physical Class.- Application of Artificial Neural Network in Social Media Data Analysis: A Case of Lodging Business in Philadelphia.- Sentiment Analysis on Morphologically Rich Languages - An Artificial Neural Network (ANN) Approach.- Predicting Stock Price Movements with News Sentiment: An Artificial Neural Networks Approach.- Modelling Mode Choice of Individual In Linked Trips with Artificial Neural Networks and Fuzzy Representation.- Artificial Neural Network (ANN) Pricing Model for Natural Rubber Products Based on Climate Dependencies.- A Hybrid Artificial Neural Network (ANN) Approach to Spatial and Non-Spatial Attribute Data Mining: A Case Study Experience.
Описание: The two volume set, LNCS 9886 + 9887, constitutes the proceedings of the 25th International Conference on Artificial Neural Networks, ICANN 2016, held in Barcelona, Spain, in September 2016. The 121 full papers included in this volume were carefully reviewed and selected from 227 submissions.
Описание: The two volume set, LNCS 9886 + 9887, constitutes the proceedings of the 25th International Conference on Artificial Neural Networks, ICANN 2016, held in Barcelona, Spain, in September 2016. The 121 full papers included in this volume were carefully reviewed and selected from 227 submissions.
Автор: Petia Koprinkova-Hristova; Valeri Mladenov; Nikola Название: Artificial Neural Networks ISBN: 3319099027 ISBN-13(EAN): 9783319099026 Издательство: Springer Рейтинг: Цена: 34937.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.
Автор: Stefan Wermter; Cornelius Weber; Wlodzislaw Duch; Название: Artificial Neural Networks and Machine Learning -- ICANN 2014 ISBN: 3319111787 ISBN-13(EAN): 9783319111780 Издательство: Springer Рейтинг: Цена: 13416.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks;
Автор: Alessandra Lintas; Stefano Rovetta; Paul F.M.J. Ve Название: Artificial Neural Networks and Machine Learning – ICANN 2017 ISBN: 3319686119 ISBN-13(EAN): 9783319686110 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions.
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