This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.

Immune system protects living body from an extraordinarily large variety of bacteria, viruses, and other pathogenic organisms. Based on immunological principles, new computational techniques are being developed, aiming not only at a better understanding of the system, but also at solving engineering problems. Our overall goal for this paper is twofold: to understand the real immune system from the information processing perspective, and to use idea generated from the immune system to construct new engineering application. As one example of the latter, we propose an artificial immune system architecture inspired by the human immune system and apply it to pattern recognition. We test the proposed architecture by the simulations on arbitrary sequences of analog input pattern classification and binary input pattern recognition. The simulation results illustrate that the proposed architecture is effective at clustering arbitrary sequences of analog input patterns into stable categories and it can produce stronger noise immunity than the binary network .

A new multilayer artificial neural network learning algorithm based on the pattern search method is proposed. The learning algorithm is designed to provide a very simple and effective means of searching the minima of an objective function directly without any knowledge of its derivatives. We test this algorithm on benchmark problems, such as exclusive-or (XOR), parity and alphabetic character learning problems. For all problems, the systems are shown to be trained efficiently by our algorithm. As a simple direct search algorithm, it can be applied to hardware implementations easily.

In this paper, we provided a new variant of Weil pairing on a family of genus 2 curves with the efficiently computable automorphism. Our pairing can be considered as a generalization of the omega pairing given by Zhao et al. We also report the algebraic cost estimation of our pairing. We then show that our pairing is more efficient than the variant of Tate pairing with the automorphism given by Fan et al. Furthermore, we show that our pairing is slightly better than the twisted Ate pairing on Kawazoe-Takahashi curve at the 192-bit security level.

This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).

To develop human interface for virtual environment, we have constructed a tensed strings based interface device called SPIDAR, which allow us to manipulate virtual object directly just like in real space. SPIDAR can both measure the movement of user's finger tip and offer force display. Since proper force feedback comes out of the proper position measurement, in this paper, we will analyze the possible reasons that may cause position measurement error, and propose an algorithm which can revise the error and improve position measurement precision.

An improved algorithm of Guided Local Search called objective function adjustment algorithm is proposed for combinatorial optimization problems. The performance of Guided Local Search is improved by objective function adjustment algorithm using multipliers which can be adjusted during the search process. Moreover, the idea of Tabu Search is introduced into the objective function adjustment algorithm to further improve the performance. The simulation results based on some TSPLIB benchmark problems showed that the objective function adjustment algorithm could find better solutions than Local Search, Guided Local Search and Tabu Search.

We have proposed a new string-based haptic interface device in this paper. It is a kind of device that allows a user to use both hands and multi-fingers to direct manipulate the virtual objects in the computer simulated virtual environment. One of the advantages of the device is to allow the user to use both hands to perform the cooperative works of hands, such as holding a large object that cannot be grasped or held by single hand. In addition, the haptic feedback sensation provided by the device at the fingertips makes possible for the user to perform dexterous manipulation, such as manipulating small size of objects. We have discussed about the design of the proposed device and have elaborated the methods of fingertip positions measurement and force feedback generation. The experiments had been carried out to verify the capabilities of the proposed device.

It is generally believed that one major function of the immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in the immune system are not only the molecules, but also their interactions. The immune cells can respond either positively or negatively to the recognition signal. A positive response would result in cell proliferation, activation and antibody secretion, while a negative response would lead to tolerance and suppression. Depending upon these immune mechanisms, an immune network model (here, we call it the binary immune network) based on the biological immune response network was proposed in our previous work. However, there are some problems like that input and memory were all binary and it did not consider the antigen diversity of immune system. To improve these problems, in this paper we propose a fuzzy immune network model by considering the antigen diversity of immune system that is the most important property to be exhibited in the immune system. As an application, the proposed fuzzy immune network is applied to pattern recognition problem. Computer simulations illustrate that the proposed fuzzy immune network model not only can improve the problems existing in the binary immune network but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories.