According to the complex control process of grinding, an expert system for grinding control based on fuzzy control and artificial neural network process was designed. The system integrates compute intelligence, expert...
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According to the complex control process of grinding, an expert system for grinding control based on fuzzy control and artificial neural network process was designed. The system integrates compute intelligence, expert system and automatic control technology, relying on powerful development environment. NET, using the advanced object-oriented language C++ and expert system tool CLIPS to develop the system. Under the guidance of expert experience and knowledge, the system has high accuracy control and run flexible stability. In the mean time it has strong adaptability and self-learning capability.
Fuzzy neural network combines the learning capacity of artificial neural networks with the interpretability of the fuzzy systems. A novel structure learning algorithm for fuzzy neural networks (SLNN) is presented in t...
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Fuzzy neural network combines the learning capacity of artificial neural networks with the interpretability of the fuzzy systems. A novel structure learning algorithm for fuzzy neural networks (SLNN) is presented in this paper. The neurons of SLNN are created and adapted as online learning proceeds. The learning rule of SLNN is based on Hebb as well as soft competitive learning. The soft competitive learning cannot only let SLNN be able to learn from new data but also prevent it from losing the knowledge that has been learned earlier. To obtain a concise fuzzy rule, a pruning algorithm is adopted in SLNN, which does not disobey the basic design philosophy of fuzzy system. Simulations are performed on the primary benchmarks: circle-in-the-square, two spirals apart, UCI machine learning archive's synthetic control chart time series, and KDDCUP'99 data set. Compared with fuzzy ARTMAP, BP and hierarchical neuro-fuzzy quadtree (HNFQ), the fuzzy neural network achieves higher performance.
Most of real-life scheduling problems are semi online. Recently, how to solve such dynamic problems is a hot topic in the research of artificial intelligence. The semi on-line scheduling is introduced and the constrai...
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Most of real-life scheduling problems are semi online. Recently, how to solve such dynamic problems is a hot topic in the research of artificial intelligence. The semi on-line scheduling is introduced and the constraint models are analyzed and categorized. By defining the relevant concept of monotony about constraints that appear in general dynamic constraint models, a kind of constraint extending is formalized. Based on this dynamic constraint modeling, a sound dynamic constraint solving algorithm is designed to deal with the scheduling problems. Finally, an application example of semi on-line discrete resource scheduling problems is given. Experiments show that the algorithm is valid.
In an open multi-agent of dynamic network environment, different mobile agents hope to carry on the communication for the problem of a certain domain. There is a need to avoid appearance of logical exception phenomeno...
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In an open multi-agent of dynamic network environment, different mobile agents hope to carry on the communication for the problem of a certain domain. There is a need to avoid appearance of logical exception phenomenon, and the term in this domain must be made to obtain consistency. A layer ontology services communication model named LOSCM is proposed in this paper. LOSCM has two primary advantages. First, it fully takes into account the factors that affect the communication, and using ontology can be represented in agent's knowledge base. Second, provided ontology does not belong to the public data source or have public ontologies, according to the concept loss degree and the concept related degree, LOSCM establishes a layering algorithm, which is able to avoid logical exception handling policies, ensure the right understanding of communication entity concept, and resolve the consistency problem of concept translation. It is shown by experiments that LOSCM is superior to other methods, and can effectively improve the communication consistency of a mobile agent system under certain conditions.
For the larger search space when learning clause in Inductive Logic Programming, we defined the clause template. Firstly, we learn the clause templates by Genetic Algorithm, and then convert it to the requisite clause...
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For the larger search space when learning clause in Inductive Logic Programming, we defined the clause template. Firstly, we learn the clause templates by Genetic Algorithm, and then convert it to the requisite clauses by combining tag matrix and information gain sampling. We designed the corresponding fitness function and genetic operators. Theoretical analysis and experiment comparison show that this algorithm can reduce the search space, improve the search efficiency and can learn recursion clause. It is an effective clause learning algorithm.
Constraint satisfaction problems (CSPs) is an important research branch in artificial intelligence. Recently, dynamic CSP is proposed as a powerful tool for solving many real-world problems on dynamic environments. As...
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Constraint satisfaction problems (CSPs) is an important research branch in artificial intelligence. Recently, dynamic CSP is proposed as a powerful tool for solving many real-world problems on dynamic environments. As a result, several algorithms to solve dynamic CSPs are presented. Among those algorithms, local change (LC) algorithm based on solution reuse strategy is a method for solving many kinds of dynamic CSPs and efficient for flexible planning. On the basis of LC algorithm which is widely used, the tabu search strategy is integrated and a mini-conflict repair based algorithm is proposed, which is called Tabu_LC. The improved algorithm considers all the conflict variables as a whole, and then solves the sub-problems with branch and bound algorithm to find the best neighbor assignment, which improves the efficiency markedly. Furthermore, the Tabu_LC algorithm is implemented in the framework of constraint solving system Ming-yue 1.0, and compared with the LC algorithm using large amount of random CSPs. The experiment indicates that the improved algorithm has overwhelmed the LC algorithm on both the efficiency and quality of solutions.
An EEG signal detection ensemble system to solve the low rate of vision detection is developed when analysis so many EEG signals. A novel FastICA method is presented, in which the independent component analysis approa...
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Color features are important to pictures and they are easy to ***, the features are widely used in content-based image retrieval (CBIR)[4][7].In the meantime, it lacks space *** this paper, color spaces are analyzed a...
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Color features are important to pictures and they are easy to ***, the features are widely used in content-based image retrieval (CBIR)[4][7].In the meantime, it lacks space *** this paper, color spaces are analyzed and YUV color space is *** and texture features are extracted in segmentation block, so there are space *** color, major segmentation block, a new kind of color quantization and a new Gray scale co-existing matrix's method are *** approach is described in detail and compared with other methods presented in the literature to deal with the same *** experiments are finished and show that the method in this paper is effective and efficient.
Geographical Information System (GIS) integrates operation and synthesis analysis of multi-factor information, satisfying the demand of high efficiency and visual performance in human computer Interaction, whereas Pee...
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The difficulties of modeling complex knowledge system lie in a large quantity of knowledge rules and the difficulty in organizing rules and grasping their mutual logical relationships. This article proposed a concept ...
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The difficulties of modeling complex knowledge system lie in a large quantity of knowledge rules and the difficulty in organizing rules and grasping their mutual logical relationships. This article proposed a concept of hierarchical fuzzy Petri nets (HFPN), which is more suitable for modeling complex knowledge system than other fuzzy Petri net models. In addition, the concepts of abstract place and abstract transition in HFPN are allowed to describe and analyze the knowledge system at diverse abstract levels. Therefore, using HFPN, the iterative and incremental methods can be applied to modeling complex knowledge system. In addition, structured approach can be naturally applied to the process of modeling knowledge system.
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