Narrow Results

We propose a new method to categorize continuous numeric percepts for Q-learning, where percept vectors are classified into categories on the basis of fuzzy ART and Q-learning uses categories as states to acquire rules for agent behavior. For efficient learning, we modify fuzzy ART to reduce the number of categories without deteriorating the efficiency of reinforcement learning. In our modification, a vigilance parameter is defined for each category in order to control the size of a category and it is updated during learning. The method to update a vigilance parameter is based on category integration, which contributes to reducing the number of categories. Here, we define the similarity for any category pair to judge whether category integration should be performed or not. When two categories are integrated into a new category, a vigilance parameter for the category is calculated and categories used for integration are discarded, so that the number of categories is reduced without restricting the number of categories. Experimental results show that Q-learning with modified fuzzy ART acquires good rules for agent behavior more efficiently than Q-learning with ordinary fuzzy ART, although the number of categories generated by modified fuzzy ART is much less than that generated by ordinary one.

Data mining is widely used to solve real-world problems in engineering, science and business. Usually the results from data mining obtained through the traditional approaches are not interpretable in the real life scenario. This paper suggests an approach for logical interpretations of the clustered data. Our approach involves using fuzzy ART technique for clustering the data and then applying the soft regression technique for interpreting the results of the clustering. The proposed model provides better analysis of data for describing overlapping clusters. We used our model to analyze patterns in the advances data of a public sector bank. Analyses and experiments show the effectiveness of the proposed method.

In this paper, an image clustering method that is essential for content-based image retrieval in large image databases efficiently is proposed by color, texture, and shape contents. The dominant triple HSV (Hue, Saturation, and Value), which are extracted from quantized HSV joint histogram in the image region, are used for representing color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Due to its algorithmic simplicity and the several merits that facilitate the implementation of the neural network, Fuzzy ART has been exploited for image clustering. Original Fuzzy ART suffers unnecessary increase of the number of output neurons when the noise input is presented. Therefore, the improved Fuzzy ART algorithm is proposed to resolve the problem by differently updating the committed node and uncommitted node, and checking the vigilance test again. To show the validity of the proposed algorithm, experimental results on image clustering performance and comparison with original Fuzzy ART are presented in terms of recall rates.

Value function approximation plays an important role in reinforcement learning (RL) with continuous state space, which is widely used to build decision models in practice. Many traditional approaches require experienced designers to manually specify the formulization of the approximating function, leading to the rigid, non-adaptive representation of the value function. To address this problem, a novel Q-value function approximation method named ‘Hierarchical fuzzy Adaptive Resonance Theory’ (HiART) is proposed in this paper. HiART is based on the Fuzzy ART method and is an adaptive classification network that learns to segment the state space by classifying the training input automatically. HiART begins with a highly generalized structure where the number of the category nodes is limited, which is beneficial to speed up the learning process at the early stage. Then, the network is refined gradually by creating the attached sub-networks, and a layered network structure is formed during this process. Based on this adaptive structure, HiART alleviates the dependence on expert experience to design the network parameter. The effectiveness and adaptivity of HiART are demonstrated in the Mountain Car benchmark problem with both fast learning speed and low computation time. Finally, a simulation application example of the one versus one air combat decision problem illustrates the applicability of HiART.