Hybrid Intelligent Computing Techniques And Its Applications

The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. This dissertation is focused on the solving the series of hybrid intelligent computing methods based on soft computing technologies consisting of neural networks, fuzzy logic systems, evolutionary algorithms, immune algorithms, quantum algorithms and kernel-based learning machines, and applying these hybrid methods to some practical problems, such as image processing, target recognition and nonlinear system identification.The main contents of the dissertation are summarized as follows:1. We firstly compare how a quantum search algorithm running hi a quantum computer differs from an evolutionary search algorithm running on a classical computers for solving NP problems by using the instance of TSP. Then, we take the knapsack problem as an example to make qualitative analysis of the characteristics of quantum-inspired evolutionary algorithm (QEA) through combination of these two techniques. It is shown that QEA can represent linear superposition of states since it is based on the concept and principles of quantum computing such as quantum bit and linear superposition of states. In addition, QEA has a better characteristic of diversity and global search than classical approaches due to its probabilistic representation.2. An adaptive immune evolutionary algorithm (AIEA) is proposed, and is applied to the image segmentation problem. AIEA can adaptively extract useful information from genes of the current optimal individual and make vaccines during evolution. At the same time, AIEA introduces an adaptive tuned parameter to denote the fraction of individuals in the current population to be subjected to the vaccination operation. This parameter is incremented by a small amount after each generation. Eventually it is equal to 1, which means all individuals have vaccination. So the very late stages of AIEA are characterized by a large number of local hill-climbing moves. Experimental results show that AIEA performs well in terms of quality of the final segmented image and robustness to noise.3. Immune concepts and methods are led into quantum-inspired evolutionary algorithm (QEA), and a novel algorithm, the immune quantum evolutionary algorithm (IQEA) is presented. On condition of preserving QEA's advantages, it utilizes some characteristics and knowledge in the pending problems for restraining the" repeat and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results on the knapsack problem show that the performance of IQEA is superior to the conventional evolutionary algorithm, the immune evolutionary algorithm and QEA. IQEA is also used to the problem of edge detection, and obtains satisfactory results.4. Based on the combination of the wavelet transform and an evolutionary neural network, we introduce a hybrid approach for radar target recognition by the range profiles. We firstly employ the wavelet transform to extract and select features from the high feature space by taking into account the non-stationary characteristic of the radar echoes. Then, we design a feed-forward neural network as classifier by using a hybrid evolutionary algorithm based on evolutionary programming. This hybrid algorithm can evolve very compact network structure, and the network classifier thus has good generalization ability.5. A fast method for radar identification by range profiles is proposed based on the kernel algorithms. The whole recognition process consists of two stages. The first is concerned with feature extraction where the kernel principal component analysis is used to select the nonlinear features of range profiles. The second is concerned with pattern classification where the proximal support vector machine is constructed as classifier. Experiment results indicate that the propos...