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"The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named, theoretical neural computation, information and optimization, from neurons to neuromorphism, spiking dynamics, from single neurons to networks, complex firing patterns, movement and motion, from sensation to perception, object and face recognition, reinforcement learning, bayesian and echo state networks, recurrent neural networks and reservoir computing, coding architectures, interacting with the brain, swarm intelligence and decision-making, mulitlayer perceptrons and kernel networks, training and learning, inference and recognition, support vector machines, self-organizing maps and clustering, clustering, mining and exploratory analysis, bioinformatics, and time weries and forecasting."

"The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named, theoretical neural computation, information and optimization, from neurons to neuromorphism, spiking dynamics, from single neurons to networks, complex firing patterns, movement and motion, from sensation to perception, object and face recognition, reinforcement learning, bayesian and echo state networks, recurrent neural networks and reservoir computing, coding architectures, interacting with the brain, swarm intelligence and decision-making, mulitlayer perceptrons and kernel networks, training and learning, inference and recognition, support vector machines, self-organizing maps and clustering, clustering, mining and exploratory analysis, bioinformatics, and time weries and forecasting."

"The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised full papers presented were carefully reviewed and selected from numerous submissions. The contributions are structured in topical sections on mathematical modeling, neurodynamics, cognitive neuroscience, learning algorithms, optimization, pattern recognition, vision, image processing, information processing, neurocontrol, and novel applications."

"As an extension of artificial intelligence research, artificial neural networks (ANN) aim to simulate intelligent behavior by mimicking the way that biological neural networks function. In Artificial Neural Networks, an international panel of 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. In the tradition of the highly successful Methods in Molecular Biology series, this volume exhibits clear, easy-to-use information with many step-by-step laboratory protocols."

"The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised full papers presented were carefully reviewed and selected from numerous submissions. The contributions are structured in topical sections on mathematical modeling, neurodynamics, cognitive neuroscience, learning algorithms, optimization, pattern recognition, vision, image processing, information processing, neurocontrol, and novel applications."

"Identifikasi sistem dinamik merupakan tahapan awal dalam melakukan perancangan algoritma kendali pada suatu sistem dinamik. Namun, pada sistem dinamik yang multivariabel, tidak linier dan kopling tinggi-seperti pada misil AIM-9L Sidewinder-identifikasi sistem dinamik umumnya akan gagal dan sering terjadi simplifikasi pada sistem yang diidentifikasi, seperti dekopling dan linearisasi sistem. Pada penelitian ini, identifikasi sistem dinamik misil dilakukan dengan menggunakan algoritma artificial neural network dengan harapan karakteristik sistem dinamik tetap terjaga dengan baik. Penerbangan misil dilakukan dengan menggunakan simulator X-Plane dan akuisisi data penerbangannya dilakukan menggunakan bahasa pemrogramman python. Penerbangan dilakukan dengan sinyal referensi swept-sine dan zig-zag untuk mancakup banyak kemungkinan penerbangan misil. Hasilnya, artificial neural networks dapat melakukan pemetaan pola sistem dinamik misil dengan standardized MSE 7.155x10^(-2).

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Dynamical system identification is the very first step in designing a control algorithm on a dynamic system. However, in the multivariate, nonlinear and coupled dynamical system-like the AIM-9L Sidewinder missile-dynamical system identifications are often failed and oversimplified the dynamical system, such as decoupling and linearization. In this research, system identification is done by using artificial neural networks algorithm with expectations that its characteristics will be maintained well. The missile flights are done by using the X-Plane flight simulator and the acquisition process is done by using python language. The flights use swept sine and zig-zag references to cover lots of missile flight conditions possibility. As a result, artificial neural networks can do missile dynamical pattern mapping with 7.155x10^(-2) standardized mean squared errors."