Advances in Computational Intelligence

This two-volume set LNCS 7902 and 7903 constitutes the refereed proceedings of the 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, held in Puerto de la Cruz, Tenerife, Spain, in June 2013. The 116 revised papers were carefully reviewed and selected from numerous submissions for presentation in two volumes. The papers explore sections on mathematical and theoretical methods in computational intelligence, neurocomputational formulations, learning and adaptation emulation of cognitive functions, bio-inspired systems and neuro-engineering, advanced topics in computational intelligence and applications

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Inhalt

It's as Easy as ABC: Introducing Anthropology-Based Computing.- Extreme Learning Machine: A Robust Modeling Technique? Yes!.- A Novel Framework to Design Fuzzy Rule-Based Ensembles Using Diversity Induction and Evolutionary Algorithms-Based Classifier Selection and Fusion.- Using Nonlinear Dimensionality Reduction to Visualize Classifiers.- Which Dissimilarity Is to Be Used When Extracting Typologies in Sequence Analysis? A Comparative Study.- Implementation of the C-Mantec Neural Network Constructive Algorithm in an Arduino Uno Microcontroller.- A Constructive Neural Network to Predict Pitting Corrosion Status of Stainless Steel.- Robust Sensor and Actuator Fault Diagnosis with GMDH Neural Networks.- Diffusion Methods for Wind Power Ramp Detection.- Computational Study Based on Supervised Neural Architectures for Fluorescence Detection of Fungicides.- Study of Alternative Strategies to Selection of Peer in P2P Wireless Mesh Networks.- A Cloud-Based Neural Network Simulation Environment.- Performance Evaluation over Indoor Channels of an Unsupervised Decision-Aided Method for OSTBC Systems.- On Second Language Tutoring through Womb Grammars.- Simulated Annealing for Real-Time Vertical-Handoff in Wireless Networks.- Ant Colony Optimization Inspired Algorithm for 3D Object Segmentation.- Alternative OVA Proposals for Cooperative Competitive RBFN Design in Classification Tasks.- A Genetic Algorithms-Based Approach for Optimizing Similarity Aggregation in Ontology Matching.- Self-organized Learning by Self-Enforcing Networks.