원문정보
초록
영어
Aiming at the problem of traditional fuzzy C-means clustering algorithm that it is sensitive to the initial clustering centers and easy to fall into the local optimization, an improved algorithm that combines Improved Quantum Genetic Optimization with FCM algorithm is proposed. In this study, chromosomes are comprised of quantum bits encoded by real number. Chromosomes are renovated by quantum rotating gates and mutated by quantum hadamard gate. The gradients of object function are utilized in adjusting the value of rotating angle by a dynamic strategy. Each chain of genes represents a optimization result, Therefore, a double searching space is acquired for the same number of chromosomes. Experimental results show that the proposed method improves the stability and the accuracy of classification．
목차
1. Introduction
2. Fuzzy Clustering
2.1. Fuzzy C-Means
3. Quantum Optimization Algorithm
3.1. Quantum Bit
3.2. Quantum Chromosome Encoding
4. Fuzzy Clustering Algorithm based on Improved Quantum Genetic Optimization
4.1. Quantum Coding and the Solution Space Transformation
4.2. Quantum Revolve Gate
4.3. Quantum Mutation
4.4. Fitness Function
4.5. Procedure of IQGA
5. Experimental Simulations and Analysis
5.1. Experimental Data Set
5.2. Experimental Testing and Results Analysis
6. Conclusion
References