This paper proposes an artificial DNA assisted queen bee genetic algorithm (DNA+QBGA) to learn the gains, control structures, membership functions, and rules of the fuzzy controller. The queen bee genetic algorithm (QBGA) possesses simple and fast evolution process to figure out the best parameters and the DNA computing is adopted to determine the structure of fuzzy controller. Each fuzzy control structure can be defined by a different bee hive, which contains the control structure and dimension of the gain. The presented DNA+QBGA can make the membership functions and rules communicate with one another among different control structures. Moreover, a novel three-step crossover operation is investigated such that the crossover between different odd dimensions of membership functions can be made.Step one is that the dimensions of parents (queen and drone) and the offspring (brood) are expanded to the same dimension resolved by their least common multiple.Step two is to randomly select the genes from the parents in the corresponding space.Step three is that the offspring gene is calculated by the real-code crossover between their parents. Finally, the simulation results of the fuzzy controlled cart-pole and chaotic systems demonstrate the feasibility and effectiveness of the proposed schemes.

The most attractive form of future network content will be multi-media including speech information, and such speech information usually carries the core concepts for the content. As a result, the spoken documents associated with the multi-media content very possibly can serve as the key for retrieval and browsing. This paper presents a new approach of hierarchical topic organization and visual presentation of spoken documents for such a purpose based on the Probabilistic Latent Semantic Analysis (PLSA). With this approach the spoken documents can be organized into a two-dimensional tree (or multi-layered map) of topic clusters, and the user can very efficiently retrieve or browse the network content or associated spoken documents. Different from the conventional document clustering approaches, with PLSA the relationships among the topic clusters and the appropriate terms as the topic labels can be very well derived. An initial prototype system with Chinese broadcast news as the example spoken documents including automatic generation of titles and summaries and retrieval/browsing functionalities is also presented. Choice of different units other than words to be used as the terms in the processing is also considered in the system based on the special structure of the Chinese language.