Academic
Publications
A fuzzy neural network and its application to pattern recognition

A fuzzy neural network and its application to pattern recognition,10.1109/91.298447,IEEE Transactions on Fuzzy Systems,Hon Keung Kwan,Yaling Cai

A fuzzy neural network and its application to pattern recognition   (Citations: 77)
BibTex | RIS | RefWorks Download
Defines four types of fuzzy neurons and proposes the structure of a four-layer feedforward fuzzy neural network (FNN) and its associated learning algorithm. The proposed four-layer FNN performs well when used to recognize shifted and distorted training patterns. When an input pattern is provided, the network first fuzzifies this pattern and then computes the similarities of this pattern to all of the learned patterns. The network then reaches a conclusion by selecting the learned pattern with the highest similarity and gives a nonfuzzy output. The 26 English alphabets and the 10 Arabic numerals, each represented by 16×16 pixels, were used as original training patterns. In the simulation experiments, the original 36 exemplar patterns were shifted in eight directions by 1 pixel (6.25% to 8.84%) and 2 pixels (12.5% to 17.68%). After the FNN has been trained by the 36 exemplar patterns, the FNN can recall all of the learned patterns with 100% recognition rate. It can also recognize patterns shifted by 1 pixel in eight directions with 100% recognition rate and patterns shifted by 2 pixels in eight directions with an average recognition rate of 92.01%. After the FNN has been trained by the 36 exemplar patterns and 72 shifted patterns, it can recognize patterns shifted by 1 pixel with 100% recognition rate and patterns shifted by 2 pixels with an average recognition rate of 98.61%. The authors have also tested the FNN with 10 kinds of distorted patterns for each of the 36 exemplars. The FNN can recognize all of the distorted patterns with 100% recognition rate. The proposed FNN can also be adapted for applications in some other pattern recognition problems
Journal: IEEE Transactions on Fuzzy Systems - TFS , vol. 2, no. 3, pp. 185-193, 1994
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
    • ...Several methods have been developed to support decision making using human judgments, including IF-THEN rule-based methods [7], fuzzy IF-THEN rule-based methods [8]–[10], and rule-based expert systems [11], [12]...

    Zhi-Jie Zhouet al. Online Updating Belief-Rule-Base Using the RIMER Approach

    • ...Fuzzy reasoning methods have been applied in intelligent systems [13], [14], especially in applications that integrate hybrid methods, including evolutionary computing [15], [16], decision trees [17], neural networks [18], data mining [19], and others [20]‐[22]...

    Honghai Liu. A Fuzzy Qualitative Framework for Connecting Robot Qualitative and Qua...

    • ...The grid-type fuzzy partition of the input space (Ishibuchi and Nakashima, 2001; Ishibuchi et al, 1995; Ishibuchi et al, 2001; Jang, 1992; Lee et al, 2001; Nozaki et al, 1996) and the scatter-type fuzzy partition (Abe and Lan, 1995; Abe and Thawonmas, 1997; Kwan and Cai, 1994; Lin and Lin, 1997; Simpson, 1992a 1992b; Su, 1993) of the input data shown in Figure <0001" ref-type="fig">1 have been often used to model fuzzy classifiers for training patterns...

    Chia-chong Chen. Design of a Fuzzy Min-max Hyperbox Classifier Using a Supervised Learn...

    • ...The grid-type fuzzy partition of the input space (Ishibuchi et al., 1995; Ishibuchi et al., 1999; Ishibuchi and Nakashima, 2001; Jang, 1992; Lee et al., 2001; Nozaki et al., 1996) and the scattertype fuzzy partition of the input data (Abe and Lan, 1995; Abe and Thawonmas, 1997; Kwan and Cai, 1994; Lin and Lin, 1997; Simpson, 1992; Su, 1993) shown in Fig. 1 have been often used to model fuzzy classifiers for training patterns...

    Unknown. A hybrid SVM and supervised learning approach to fuzzy min‐max hyperbo...

    • ...Fuzzy neural network has been studied by several research and different type of fuzzy neural networks have been proposed and analyzed [1,2,3,4]...

    Rui Fanget al. A novel fuzzy neural network: the vague neural network

Sort by: