This text provides basic concepts, algorithms and analysis of neural network models, with emphasis on the importance of knowledge in intelligent system design. It bridges the gap between artificial intelligence annd neural networks. The text provides a unified perspective, which could be used to integrate intelligence technologies.
Code Recognition and Set Selection with Neural Networks (Mathematical Modeling, v. 7)
by Clark Jeffries
Joint Source-Channel Coding of Discrete-Time Signals with Continuous Amplitudes (Communications and Signal Processing, #1)
by Norbert Goertz
This book provides the first comprehensive and easy-to-read discussion of joint source-channel encoding and decoding for source signals with continuous amplitudes. It is a state-of-the-art presentation of this exciting, thriving field of research, making pioneering contributions to the new concept of source-adaptive modulation.The book starts with the basic theory and the motivation for a joint realization of source and channel coding. Specialized chapters deal with practically relevant scenario...
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow
by Geron Aurelien
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clari...
Proceedings of the Thirtieth International MATADOR Conference
The collected papers presented at the Thirtieth International Matador Conference, held at UMIST, Manchester on 31 March-1 April 1993. This year, in addition to the latest developments in traditional areas of machine tool technology, sessions on expert systems and neural networks have been included for the first time, reflecting the growing importance of artificial intelligence applications in manufacturing. Contains contributions from the following countries: Japan, USA, Hungary, Italy, Poland,...
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size--small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who...
Differential Neural Networks for Robust Nonlinear Control: Identification, State Estimation and Trajectory Tracking
by Alexander S Poznyak, Edgar N. Sanchez, and Wen Yu
This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different...
Feedforward Neural Network Methodology (Information Science and Statistics)
by Terrence L. Fine
This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly suc...
Advanced Topics in Neural Networks with Matlab. Parallel Computing, Optimize and Training
by Perez C
Parallel Architectures And Neural Networks - Third Italian Workshop
Hands-On Markov Models with Python
by Ankur Ankan and Abinash Panda
Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearnKey FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you...
Intelligent Engineering Systems through Artificial Neural Networks Vol 10
This newest volume in the series presents refereed papers covering the following eight categories and their applications in the engineering domain. -- Neural Networks -- Complex Systems -- Evolutionary Programming -- Data Mining -- Fuzzy Logic -- Adaptive Control -- Pattern Recognition -- Smart Engineering System Design These papers are intended to provide a forum for researchers in the field to exchange ideas on smart engineering systems design.
