Titlebook: Artificial Intelligence and Soft Computing; 14th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

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Toward Work Groups Classification Based on Probabilistic Neural Network Approach proposed bio-inspired solution analyses employees using data gathered from their professional attitudes and skills, then suggests how to form groups of human resources within a company that can effectively work together. The same proposed tool provides employers with a fair and effective means for

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Adaptation of RBM Learning for Intel MIC Architectures of modern CPUs and Intel Xeon Phi coprocessor. The learning procedure is based on the matrix description of RBM, where the learning samples are grouped into packages, and represented as matrices. The influence of the package size on convergence of learning, as well as on performance of computation

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Using an Artificial Neural Network to Predict Loop Transformation Timereaded code. However, the time of a transformation process is usually unknown, especially for transitive closure based algorithms. The computational complexity of transitive closure calculation algorithms is relatively high and may prevent applying corresponding transformations. The paper presents t

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Product Multi-kernels for Sensor Data Analysisn this paper, we focus on its extension with multi-kernel units. In particular, we describe the architecture of a product unit network, and we propose an evolutionary learning algorithm for setting its parameters. The algorithm is capable to select different kernels from a dictionary and to set thei

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A Fuzzy Approach to Competitive Clusters Using Moore Familiesties in a specific city. The aim is to create clusters towards the construction of competitive advantages, cost reduction and economies of scale. We utilize tools of Fuzzy Sets Theory, evaluating productive capacities of local enterprises under Moore Families. Results conclude in 16 different cluste

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Learning Rules for Type-2 Fuzzy Logic System in the Control of DeNOx Filter of fuzzy inference systems, this article focuses on presenting new learning methods for fuzzy rules. Referring to earlier work, the authors extended learning methods for fuzzy rules on applications of Type-2 fuzzy logic systems to control filters reducing air pollution. The filters use Selective Ca