The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011. The total of 215 papers presented in all three volumes were carefully reviewed and selected from 651 submissions. The contributions are structured in topical sections on computational neuroscience and cognitive science; neurodynamics and complex systems; stability and convergence analysis; neural network models; supervised learning and unsupervised learning; kernel methods and support vector machines; mixture models and clustering; visual perception and pattern recognition; motion, tracking and object recognition; natural scene analysis and speech recognition; neuromorphic hardware, fuzzy neural networks and robotics; multi-agent systems and adaptive dynamic programming; reinforcement learning and decision making; action and motor control; adaptive and hybrid intelligent systems; neuroinformatics and bioinformatics; information retrieval; data mining and knowledge discovery; and natural language processing.

In this book, international experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. Methods involving the mapping and interpretation of Infra Red spectra and modelling environmental toxicology are included. This book is an excellent guide to this exciting field.

The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers.

Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

A cursory glance at the table of contents of EANN 2009 reveals the am- ing range of neural network and related applications. A random but revealing sample includes: reducing urban concentration, entropy topography in epil- tic electroencephalography, phytoplanktonic species recognition, revealing the structure of childhood abdominal pain data, robot control, discriminating angry and happy facial expressions, ?ood forecasting, and assessing credit worthiness. The diverse nature of applications demonstrates the vitality of neural comp- ing and related soft computing approaches, and their relevance to many key contemporary technological challenges. It also illustrates the value of EANN in bringing together a broad spectrum of delegates from across the world to learn from each other's related methods. Variations and extensions of many methods are well represented in the proceedings, ranging from support vector machines, fuzzy reasoning, and Bayesian methods to snap-drift and spiking neurons. This year EANN accepted approximately 40% of submitted papers for fu- length presentation at the conference. All members of the Program Committee were asked to participate in the reviewing process. The standard of submissions was high, according to the reviewers, who did an excellent job. The Program and Organizing Committees thank them. Approximately 20% of submitted - pers will be chosen, the best according to the reviews, to be extended and - viewedagainfor inclusionin a specialissueofthe journalNeural Computing and Applications. We hope that these proceedings will help to stimulate further research and development of new applications and modes of neural computing.

The 8th International Workshop on Fuzzy Logic and Applications (WILF 2009) held in Palermo (Italy), June 9-12, 2009, covered topics related to theoretical and experimental areas of fuzzy sets and systems with emphasis on di?erent applications. This eventrepresentsthe continuation ofan established traditionof biannual interdisciplinary meetings. The previous editions of WILF were held, with an increasing number of participants, in Naples (1995), Bari (1997), Genoa (1999), Milan (2001), Naples (2003), Crema (2005) and Camogli (2007). Each event focused on distinct main thematic areas of fuzzy logic and related applications. WILF 2009 aimed to highlight connections and synergiesof fuzzy sets theory with nonconventional computing (e.g., neural networks, evolutionary compu- tion, support vector machines, molecular computing, quantum computing) and cognitivescience,inordertoreachabetterunderstandingofbothnaturaland- ti?cial complex systems as well as computing systems, inspired by nature, which are able to solve complex problems. From this perspective one of the main goals of the WILF workshops is to bring together researchers and developers from both academia and high-tech companies. WILF 2009receivedmorethan 60 paper submissionsfrom all overthe world, including Algeria, Belgium, Benin, Brazil, Canada, China, France, Greece, India, Iran, Italy, Japan, Poland, Romania, Slovakia, Spain and the USA. A rigorous peer-review selection process was applied to ultimately select nearly 40 high-quality manuscripts to be published in this volume. Moreover,thevolumealsoincludespresentationsfromthreekeynotespeakers EtienneKerre(GhentUniversity,Belgium),SankarK.Pal(ISI,India)andEnric Trillas (ECSC, Spain).

The two volume set LNCS 4984 and LNCS 4985 constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Neural Information Processing, ICONIP 2007, held in Kitakyushu, Japan, in November 2007, jointly with BRAINIT 2007, the 4th International Conference on Brain-Inspired Information Technology.

The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.

These two-volume books comprise the post-conference proceedings of the 14th International Conference on Neural Information Processing (ICONIP 2007) held in Kitakyushu, Japan, during November 13-16, 2007. The Asia Paci?c Neural Network Assembly (APNNA) was founded in 1993. The ?rst ICONIP was held in 1994 in Seoul, Korea, sponsored by APNNA in collaboration with regional organizations. Since then, ICONIP has consistently provided prestigious opp- tunities for presenting and exchanging ideas on neural networks and related ?elds. Research ?elds covered by ICONIP have now expanded to include such ?elds as bioinformatics, brain machine interfaces, robotics, and computational intelligence. We had 288 ordinary paper submissions and 3 special organized session p- posals. Although the quality of submitted papers on the average was excepti- ally high, only 60% of them were accepted after rigorous reviews, each paper being reviewed by three reviewers. Concerning special organized session prop- als, two out of three were accepted. In addition to ordinary submitted papers, we invited 15 special organized sessions organized by leading researchers in emerging ?elds to promote future expansion of neural information processing. ICONIP 2007 was held at the newly established Kitakyushu Science and Research Park in Kitakyushu, Japan. Its theme was "Towards an Integrated Approach to the Brain-Brain-Inspired Engineering and Brain Science," which emphasizes the need for cross-disciplinary approaches for understanding brain functions and utilizing the knowledge for contributions to the society. It was jointly sponsored by APNNA, Japanese Neural Network Society (JNNS), and the 21st century COE program at Kyushu Institute of Technology.

State-of-the-art coverage of Kalman filter methods for the design of neural networks

This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear.

The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:

• An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)
• Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes
• The dual estimation problem
• Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm
• The unscented Kalman filter

Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.

This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007.

The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ensemble learning, spiking neural networks, advances in neural network architectures neural network technologies, neural dynamics and complex systems, data analysis, estimation, spatial and spatio-temporal learning, evolutionary computing, meta learning, agents learning, complex-valued neural networks, as well as temporal synchronization and nonlinear dynamics in neural networks.

This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007.

The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The second volume contains 99 contributions related to computational neuroscience, neurocognitive studies, applications in biomedicine and bioinformatics, pattern recognition, data clustering, self-organization, text mining and internet applications, signal and times series processing, vision and image processing, robotics, control, real world applications, independent component analysis, graphs, emotion and attention: empirical findings and neural models, as well as understanding and creating cognitive systems.

This second edition of Evolving Connectionist Systems presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems, as well as new trends including computational neuro-genetic modelling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.

The models and techniques used are connectionist-based and, where possible, existing connectionist models have been used and extended. Divided into four parts the book opens with evolving processes in nature; looks at methods and techniques that can be used in evolving connectionist systems; then covers various applications in bioinformatics and brain studies; finishing with applications for intelligent machines.

Aimed at all those interested in developing adaptive models and systems to solve challenging real world problems in computer science and engineering.

This is volume I of the proceedings of the Second International Conference on Natural Computation, ICNC 2006. After a demanding review process 168 carefully revised full papers and 86 revised short papers were selected from 1915 submissions for presentation in two volumes. This first volume includes 130 papers related to artificial neural networks, natural neural systems and cognitive science, neural network applications, as well as evolutionary computation: theory and algorithms.

The two volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006, held in Athens, Greece, in September 2006.

The 208 revised full papers presented were carefully reviewed and selected from 475 submissions. The 103 papers of the first volume are organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, learning random neural networks and stochastic agents, hybrid architectures, self organization, connectionist cognitive science, cognitive machines, neural dynamics and complex systems, computational neuroscience, neural control, reinforcement learning and robotics applications, robotics, control, planning, as well as bio-inspired neural network on-chip implementation and applications. The second volume contains 105 contributions related to neural networks, semantic web technologies and multimedia analysis, bridging the semantic gap in multimedia machine learning approaches, signal and time series processing, data analysis, pattern recognition, visual attention algorithms and architectures for perceptional understanding and video coding, vision and image processing, computational finance and economics, neural computing in energy engineering, applications to biomedicine and bioinformatics, applications to security and market analysis, as well as real world applications.

This book constitutes the refereed proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, held in San Sebastian, Spain in June 2007.

The 145 revised full papers presented together with 3 invited lectures were carefully reviewed and selected from over 260 submissions. The papers are organized in thematic sections on theoretical concepts and neurocomputational formulations, inmproving models and learning procedures, self-organizing networks, kernel methods, evolutionary and genetic algorithms, evolutionary learning, fuzzy systems, neuroengineering and hardware implementations, data analysis, signal processing, speech processing, images processing, time series and prediction, robotics and planning motor control, power system applications, internet and web applications, biomedical applications, neural networks and other machine learning methods in cancer research, assistive technologies and e-health, as well as other applications.

This book uncovers the stakes and possibilities of handling pandemic diseases with the help of Computational Intelligence, using cases and applications from the current Covid-19 pandemic. The book chapters will focus on the application of CI and its related fields in managing different aspects of Covid-19, including modelling of the disease spread, data-driven prediction, identification of disease hotspots, and medical decision support.

This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11-15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected - after a thorough peer-review process - about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.

This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11-15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected - after a thorough peer-review process - about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.

This book constitutes the thoroughly refereed postproceedings of the 16th Italian Workshop on Neural Nets, WIRN 2005, as well as the satellite International Workshop on Natural and Artificial Immune Systems, NAIS 2005, held in Vietri sul Mare, Italy in June 2005. The 41 revised papers presented together with a lecture by the winner of the Premio Caianiello award were carefully reviewed and improved during two rounds of selection and refereeing.

This volume provides students, researchers and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems. Features of particular interest include:- A NETLAB toolbox which is freely available via the Internet at http://www.ncrg.aston.ac.uk/netlab- Worked examples, demonstration programs and over 100 graded exercises- Cutting edge research made accessible for the first time in a highly usable form- Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian ProcessesAlthough primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions and new models."...provides a unique collection of many of the most important pattern recognition algorithms. With its use of compact and easily modified MATLAB scripts, the book is ideally suited to both teaching and research."Christopher Bishop, Microsoft Research, Cambridge, UK"...a welcome addition to the literature on neural networks and how to train and use them to solve many of the statistical problems that occur in data analysis and data mining" Jack Cowan, Mathematics Department, University of Chicago, US"If you have a pattern recognition problem, you should consider NETLAB; if you use NETLAB you must have this book." Keith Worden, University of Sheffield, UK

The proceedings of the 14th Italian Workshop on Neural Nets WIRN VIETRI 2003 are collected in this volume. The workshop, held in Vietri sul Mare (SA) June 4-7, 2003 was jointly organized by the International Institute for Advanced Scienti?c Studies "Eduardo R. Caianiello" (IIASS) and the Societ' a Italiana Reti Neuroniche(SIREN). Thevolumecoversup-to-datetopicsonneuralnetsand- lated ?elds. It contains invited review papers and selected original contributions presented in either oral or poster sessions by both Italian and non-Italian - searchers. The contributions have been assembled, for reading convenience, into four sections: Models, Architectures and Algorithms, Image and Signal Proce- ing, and Applications, plus two special sessions. The latter gives a fresh persp- tive in the ?elds of "Bioinformatics and Statistics" and "Formats Knowledge: Words, Images, Narratives", sharing the technical languages of the involved d- ciplines. The editors would like to thank the invited speakers and all the c- tributors whose highly quali?ed papers contributed towards the success of the workshop. Finally, special thanks go to the referees for their accurate work. June 2003 Bruno Apolloni Maria Marinaro Roberto Tagliaferri Organizing Scienti?c Committee B. Apolloni (Univ. Milano), A. Bertoni (Univ. Milano), N. A. Borghese (Univ. Milano),D. D. Caviglia(Univ. Genova),P. Campadelli(Univ. Milano),A. Chella (Univ. Palermo),A. Colla(ELSAGGenova),A. Esposito(I. I. A. S. S. ),F. M. Fr- tale Mascioli (Univ. Roma), C. Furlanello (ITC-IRST Trento), S. Giove (Univ. Venezia), M. Gori (Univ. Siena), M. Marinaro (Univ. Salerno), F. Masulli (Univ. Pisa), C. Morabito (Univ. Reggio Calabria), P. Morasso (Univ. Genova), G.

Neural networks are one of the fast-growing paradigms for learning systems with a wide variety of potential applications in industry. In particular there are general results which prove the universal applicability of neural networks to many problems. There is also an ever greater understanding of the underlying manner in which tasks such as classification can be solved optimally by this host of techniques. Through the application of ideas of statistics, dynamical systems theory and information theory the methods are likely to become ever more effective for solving problems previously found to be difficult to tackle using standard techniques. This book compares and contrasts the academic theory and the industrial reality, with case studies and latest research findings from international experts. The contributions describe application areas including finance, digital data transmission, hybrid systems, automotive and aerospace industries, pattern analysis in clinical psychiatry, time series prediction, and genetic and neural algorithms. This book demonstrates the vigour and strength of the subject in solving hard problems and as such will be of great interest to all researchers and professionals with an interest in neural networks.

The two-volume set LNCS 2686 and LNCS 2687 constitute the refereed proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, held in Maó, Menorca, Spain in June 2003. The 197 revised papers presented were carefully reviewed and selected for inclusion in the book and address the following topics: mathematical and computational methods in neural modelling, neurophysiological data analysis and modelling, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, cognitive processes and artificial intelligence, methodologies for net design, bio-inspired systems and engineering, and applications in a broad variety of fields.

The papers in this volume present theoretical aspects and applications of artificial neural networks and genetic algorithms. Also included are papers on fuzzy logic, soft computing, and artificial intelligence. Fundamental issues are addressed such as the nonlinear approximation capabilities of neural networks and formal methods of data representation with topological properties. New elements in genetic algorithms are presented, for example, crossover methods and gene representation. Papers on applications of neural networks show how successful these methods are in a wide range of fields like meteorological and atmospheric pollution forecasts, furnace control, and system identification. Genetic algorithms are used to solve optimization problems related to shipping and computer vision. Fuzzy-logic-based techniques are applied to sociodynamic models and hybrid neuro-fuzzy models.

th This volume constitutes the proceedings of the 6 Conference on Neural Networks and Soft Computing, ICNNSC'2002, held in Zakopane, Poland in June 11-15, 2002. The Conference was organized by the Polish Neural Network Society in co- operation with the Department of Computer Engineering at the Technical Univer- sity of Cz~stochowa. The previous conferences took place in Kule (1994), Szczyrk (1996), Kule (1997) and Zakopane (1999, 2000) and attracted a large number of papers and internationally recognized speakers like Prof. Robert Marks, Prof. Jacek Zurada, Prof. Ryszard Tadeusiewicz, Prof. Shiro Usui, Prof. Daniel Amit, Prof. Wlodzislaw Duch, Prof. Erkki Oja, Prof. Syozo Yasui, Prof. Enrique Ruspini, Prof. Witold Pedrycz, Prof. Janusz Kacprzyk and Prof. Lotfi A. Zadeh. It was pointed out by Prof. Lotfi A. Zadeh that "Soft Computing (SC) is a coalition of methodologies which are oriented toward the conception and design of informa- tion/intelligent systems. The principal members of the coalition are: fuzzy logic (FL), neurocomputing (NC), evolutionary computing (EC), probabilistic comput- ing (PC), chaotic computing (CC), and machine learning (ML). The constituent methodologies of SC are, for the most part, complementary and synergistic rather than competitive". This volume presents most techniques and methodologies mentioned by Prof.