Download Artificial Intelligence and Soft Computing: 13th by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard PDF

By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada

The two-volume set LNAI 8467 and LNAI 8468 constitutes the refereed court cases of the thirteenth foreign convention on synthetic Intelligence and delicate Computing, ICAISC 2014, held in Zakopane, Poland in June 2014. The 139 revised complete papers awarded within the volumes, have been rigorously reviewed and chosen from 331 submissions. The sixty nine papers incorporated within the first quantity are fascinated by the subsequent topical sections: Neural Networks and Their purposes, Fuzzy platforms and Their functions, Evolutionary Algorithms and Their purposes, class and Estimation, desktop imaginative and prescient, picture and Speech research and designated consultation three: clever tools in Databases. The seventy one papers within the moment quantity are equipped within the following topics: information Mining, Bioinformatics, Biometrics and scientific purposes, Agent platforms, Robotics and keep watch over, man made Intelligence in Modeling and Simulation, numerous difficulties of synthetic Intelligence, designated consultation 2: computer studying for visible details research and safety, specified consultation 1: purposes and houses of Fuzzy Reasoning and Calculus and Clustering.

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The proposed novel topology behaves as a cascade FFNN topology [9]. Fig. 4 depicts a more complex novel topology that performs the prediction as a Nonlinear AutoRecoursive with exogenous inputs (NARX) recurrent neural network topology [10]. Such figure shows the implemented delay lines to the blocks performing the neural processing. It should be noted that we have implemented one neuron as a purelin while the remaining neurons in the first hidden layer process the input signal. The performed simulations have shown an increased computational effort, for this recurrent scheme, while the corresponding results have not significantly improved the accuracy on the predicted data.

12–21, 2014. c Springer International Publishing Switzerland 2014 The Parallel Approach to the Conjugate Gradient Learning Algorithm 13 Fig. 1. Sample structure of the feedforward neural network Fig. 2. Recal phase of the feedforward network and the structures of processing elements elements (PE) in Fig. 2b. Two kinds of functional processing elements are used in the proposed solution. The aim of the processing elements A is to create matrices which contain values of weights in all layers. The input signals are entered for rows elements parallel, multiplied by weights and received results are summed in columns.

Rutkowski et al. ): ICAISC 2014, Part I, LNAI 8467, pp. 34–46, 2014. c Springer International Publishing Switzerland 2014 Application of Support Vector Machines, CNNs and DBNs 35 which is sufficient to perform classification or prediction tasks, but which cannot reproduce samples like a generative model can. This suggests that DBNs should perform better on the task of partially occluded object recognition, as they ought to be able to use their generative effects to partially reconstruct the image to aid in classification.

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