In this paper a novel direct clustering algorithm based on generalized information distance (GID) is put forward. Firstly, based on information theory, a basic concept of measure of diversity is given and an inequalit...
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In this paper a novel direct clustering algorithm based on generalized information distance (GID) is put forward. Firstly, based on information theory, a basic concept of measure of diversity is given and an inequality about measure of diversity is proved. Based on this inequality, a concept of increment of diversity is discussed and a defined. Secondly, by analyzing distance measure, two new concepts of generalized information distance (GID) and improved generalized information distance (IGID) are proposed, and a new direct clustering algorithm based on GID and IGID is designed. Finally this algorithm is applied to soil fertility data processing, and compared with hierarchical clustering algorithm (HCA). The results of simulation application show that the algorithm presented here is feasible and effective. Because of simplicity of algorithm and robustness. It provides a new research approach for studies of pattern recognition theory.
The contrast function remains to be an open problem in blind source separation （BSS） when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual...
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The contrast function remains to be an open problem in blind source separation （BSS） when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank. The mutual information reaches its minimum at the separation points, where the random outputs of the BSS system are the scaled and permuted source signals, while the others are zero outputs. Using the property that the transpose of the mixing matrix and a matrix composed by m observed signals have the indentical null space with probability one, a practical method, which can detect the unknown number of source signals n, ulteriorly traces the dynamical change of the sources number with a few of data, is proposed. The effectiveness of the proposed theorey and the developed novel algorithm is verified by adaptive BSS simulations with unknown and dynamically changing number of source signals.
Synthetic Aperture Radar (SAR) image despeckling is an important problem in the SAR applications. A novel despeckling approach using a local contextual hidden Markov model (LCHMM) in the contourlet domain is presented...
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Modelling mixtures of multivariate t-distributions are usually used instead of Gaussian mixture models(GMM) as a robust approach, when one fits a set of continuous multivariate data which have wider tail than Gaussian...
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This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstr...
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This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.
Gaussian Processes (GPs) have state of the art performance in regression. In GPs, all the basis functions are required for prediction;hence its test speed is slower than other learning algorithms such as support vecto...
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Particle Swarm Optimization (PSO) is gaining momentum as a simple and effective optimization technique. However, its performance on complex problem with multiple minima falls short of that of the Ant Clony Optimizatio...
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Artificial Intelligence （AI） is generally considered to be a subfield of computer science, that is concerned to attempt simulation, extension and expansion of human intelligence. Artificial intelligence has enjoyed ...
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Artificial Intelligence （AI） is generally considered to be a subfield of computer science, that is concerned to attempt simulation, extension and expansion of human intelligence. Artificial intelligence has enjoyed tremendous success over the last fifty years. In this paper we only focus on visual perception, granular computing, agent computing, semantic grid. Human-level intelligence is the long-term goal of artificial intelligence. We should do joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. A new cross discipline intelligence science is undergoing a rapid development. Future challenges are given in final section.
In chaos control fields, the nonfeedback and the nonlinear feedback approaches are inevitable much less flexible. However linear feedback controllers are easy to implement, especially in electrical systems. In this pa...
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In this paper, we offers a new algebraic point of view for DNA molecules and introduce the algebraic system by using the natural operation based on the Σ = {A, C, G, T}. We characterize its structure by using the alg...
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