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Fuzzy Holographic And Parallel Intelligence

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Fuzzy Holographic And Parallel Intelligence

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Author : Branko Soucek
language : en
Publisher: Wiley-Interscience
Release Date : 1992-05-06

Fuzzy Holographic And Parallel Intelligence written by Branko Soucek and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-05-06 with Computers categories.

This important volume introduces you to the new wave of applied intelligent systems that are able to deal with the complex, fuzzy, noisy, and highly varying problems found today in everything from manufacturing to intelligent database tasks. The sixth generation advanced hardware and software solutions in this innovative, groundbreaking work open up new vistas in computing: processing and reasoning by associations. You'll find major breakthroughs in machine cognition--speed-up factors of 10 to 100, compared to other learning paradigms, are achievable; novel solutions to the enfolding of very large sets of fuzzy associations using holographic neural technology, including a novel information representation solution employing a vector orientation on a Riemann plane; a description of neural-fuzzy hybrids, a new class of associative systems and cellular processors; a full spectrum of techniques and algorithms to vastly enhance performance in such key computer processes as scientific and technical data processing...natural language generation and cognition...fuzzy processing in reasoning...decision-making robotics and process control...computer vision and graphics...and pattern identification and recognition. Computer engineers, software designers, managers, and students will find a range of newly developed intelligent modules, systems, and methods never before gathered under one cover--plus previously unpublished work from the renowned international IRIS (Integration of Reasoning, Informing and Serving) Group. Fuzzy, Holographic and Parallel Intelligence is the sixth-generation breakthrough.

Intelligent Control Of Robotic Systems

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Author : D. Katic
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14

Intelligent Control Of Robotic Systems written by D. Katic and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Technology & Engineering categories.

As robotic systems make their way into standard practice, they have opened the door to a wide spectrum of complex applications. Such applications usually demand that the robots be highly intelligent. Future robots are likely to have greater sensory capabilities, more intelligence, higher levels of manual dexter ity, and adequate mobility, compared to humans. In order to ensure high-quality control and performance in robotics, new intelligent control techniques must be developed, which are capable of coping with task complexity, multi-objective decision making, large volumes of perception data and substantial amounts of heuristic information. Hence, the pursuit of intelligent autonomous robotic systems has been a topic of much fascinating research in recent years. On the other hand, as emerging technologies, Soft Computing paradigms consisting of complementary elements of Fuzzy Logic, Neural Computing and Evolutionary Computation are viewed as the most promising methods towards intelligent robotic systems. Due to their strong learning and cognitive ability and good tolerance of uncertainty and imprecision, Soft Computing techniques have found wide application in the area of intelligent control of robotic systems.

Innovations In Machine And Deep Learning

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Author : Gilberto Rivera
language : en
Publisher: Springer Nature
Release Date : 2023-09-28

Innovations In Machine And Deep Learning written by Gilberto Rivera and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-28 with Computers categories.

In recent years, significant progress has been made in achieving artificial intelligence (AI) with an impact on students, managers, scientists, health personnel, technical roles, investors, teachers, and leaders. This book presents numerous successful applications of AI in various contexts. The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI. Decision-makers and entrepreneurs will find numerous successful applications in health care, sustainability, risk management, human activity recognition, logistics, and Industry 4.0. This book is an essential resource for anyone interested in challenges, opportunities, and the latest developments and real-world applications of ML. Whether you are a student, researcher, practitioner, or simply curious about AI, this book provides valuable insights and inspiration for your work and learning.

Foundations Of Distributed Artificial Intelligence

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Author : G. M. P. O'Hare
language : en
Publisher: John Wiley & Sons
Release Date : 1996-04-05

Foundations Of Distributed Artificial Intelligence written by G. M. P. O'Hare and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-04-05 with Computers categories.

Distributed Artificial Intelligence (DAI) is a dynamic area of research and this book is the first comprehensive, truly integrated exposition of the discipline presenting influential contributions from leaders in the field. Commences with a solid introduction to the theoretical and practical issues of DAI, followed by a discussion of the core research topics--communication, coordination, planning--and how they are related to each other. The third section describes a number of DAI testbeds, illustrating particular strategies commissioned to provide software environments for building and experimenting with DAI systems. The final segment contains contributions which consider DAI from different perspectives.

Fuzzy Holographic And Parallel Intelligence

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Author : Branko Soucek
language : en
Publisher: Wiley-Interscience
Release Date : 1992-05-06

Artificial Neural Nets And Genetic Algorithms

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Author : Rudolf F. Albrecht
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Artificial Neural Nets And Genetic Algorithms written by Rudolf F. Albrecht and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.

Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.

Rethinking Neural Networks

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Author : Karl H. Pribram
language : en
Publisher: Psychology Press
Release Date : 2014-04-08

Rethinking Neural Networks written by Karl H. Pribram and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-08 with Psychology categories.

The result of the first Appalachian Conference on neurodynamics, this volume focuses on processing in biological neural networks. How do brain processes become organized during decision making? That is, what are the neural antecedents that determine which course of action is to be pursued? Half of the contributions deal with modelling synapto-dendritic and neural ultrastructural processes; the remainder, with laboratory research findings, often cast in terms of the models. The interchanges at the conference and the ensuing publication also provide a foundation for further meetings. These will address how processes in different brain systems, coactive with the neural residues of experience and with sensory input, determine decisions.

World Congress On Neural Networks San Diego

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Author :
language : en
Publisher: Psychology Press
Release Date : 1994

World Congress On Neural Networks San Diego written by and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Artificial intelligence categories.

The Theory Of Perfect Learning

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Author : Nonvikan Karl-Augustt Alahassa
language : en
Publisher: Nonvikan Karl-Augustt Alahassa
Release Date : 2021-08-17

The Theory Of Perfect Learning written by Nonvikan Karl-Augustt Alahassa and has been published by Nonvikan Karl-Augustt Alahassa this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-17 with Science categories.

The perfect learning exists. We mean a learning model that can be generalized, and moreover, that can always fit perfectly the test data, as well as the training data. We have performed in this thesis many experiments that validate this concept in many ways. The tools are given through the chapters that contain our developments. The classical Multilayer Feedforward model has been re-considered and a novel $N_k$-architecture is proposed to fit any multivariate regression task. This model can easily be augmented to thousands of possible layers without loss of predictive power, and has the potential to overcome our difficulties simultaneously in building a model that has a good fit on the test data, and don't overfit. His hyper-parameters, the learning rate, the batch size, the number of training times (epochs), the size of each layer, the number of hidden layers, all can be chosen experimentally with cross-validation methods. There is a great advantage to build a more powerful model using mixture models properties. They can self-classify many high dimensional data in a few numbers of mixture components. This is also the case of the Shallow Gibbs Network model that we built as a Random Gibbs Network Forest to reach the performance of the Multilayer feedforward Neural Network in a few numbers of parameters, and fewer backpropagation iterations. To make it happens, we propose a novel optimization framework for our Bayesian Shallow Network, called the {Double Backpropagation Scheme} (DBS) that can also fit perfectly the data with appropriate learning rate, and which is convergent and universally applicable to any Bayesian neural network problem. The contribution of this model is broad. First, it integrates all the advantages of the Potts Model, which is a very rich random partitions model, that we have also modified to propose its Complete Shrinkage version using agglomerative clustering techniques. The model takes also an advantage of Gibbs Fields for its weights precision matrix structure, mainly through Markov Random Fields, and even has five (5) variants structures at the end: the Full-Gibbs, the Sparse-Gibbs, the Between layer Sparse Gibbs which is the B-Sparse Gibbs in a short, the Compound Symmetry Gibbs (CS-Gibbs in short), and the Sparse Compound Symmetry Gibbs (Sparse-CS-Gibbs) model. The Full-Gibbs is mainly to remind fully-connected models, and the other structures are useful to show how the model can be reduced in terms of complexity with sparsity and parsimony. All those models have been experimented, and the results arouse interest in those structures, in a sense that different structures help to reach different results in terms of Mean Squared Error (MSE) and Relative Root Mean Squared Error (RRMSE). For the Shallow Gibbs Network model, we have found the perfect learning framework : it is the $(l_1, \boldsymbol{\zeta}, \epsilon_{dbs})-\textbf{DBS}$ configuration, which is a combination of the \emph{Universal Approximation Theorem}, and the DBS optimization, coupled with the (\emph{dist})-Nearest Neighbor-(h)-Taylor Series-Perfect Multivariate Interpolation (\emph{dist}-NN-(h)-TS-PMI) model [which in turn is a combination of the research of the Nearest Neighborhood for a good Train-Test association, the Taylor Approximation Theorem, and finally the Multivariate Interpolation Method]. It indicates that, with an appropriate number $l_1$ of neurons on the hidden layer, an optimal number $\zeta$ of DBS updates, an optimal DBS learnnig rate $\epsilon_{dbs}$, an optimal distance \emph{dist}$_{opt}$ in the research of the nearest neighbor in the training dataset for each test data $x_i^{\mbox{test}}$, an optimal order $h_{opt}$ of the Taylor approximation for the Perfect Multivariate Interpolation (\emph{dist}-NN-(h)-TS-PMI) model once the {\bfseries DBS} has overfitted the training dataset, the train and the test error converge to zero (0). As the Potts Models and many random Partitions are based on a similarity measure, we open the door to find \emph{sufficient} invariants descriptors in any recognition problem for complex objects such as image; using \emph{metric} learning and invariance descriptor tools, to always reach 100\% accuracy. This is also possible with invariant networks that are also universal approximators. Our work closes the gap between the theory and the practice in artificial intelligence, in a sense that it confirms that it is possible to learn with very small error allowed.

World Congress On Neural Networks

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Author : Paul Werbos
language : en
Publisher: Routledge
Release Date : 2021-09-10

World Congress On Neural Networks written by Paul Werbos and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-10 with Psychology categories.

Centered around 20 major topic areas of both theoretical and practical importance, the World Congress on Neural Networks provides its registrants -- from a diverse background encompassing industry, academia, and government -- with the latest research and applications in the neural network field.

Fuzzy Holographic And Parallel Intelligence

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