A type of stochastic interval delayed Hopfield neural networks as du(t) = [-AIu(t) + WIf(t, u(t)) +WI^τfτ(uτ(t))] dt + a(t,u(t),u(t)) dw(t) on t≥0 with initiated value u(s) =ξ(s)on -τ^-≤s≤0 has been studied. By using the Razumikhin theorem and Lyapunov functions, some sufficient conditions of their globally asymptotic robust stability and global exponential stability on such systems have been given. All the results obtained are generalizations of some recent ones reported in the literature for uncertain neural networks with constant delays or their certain cases.
To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing. A small job shop scheduling problem is solved in DNA computing, and the “operations” of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.
A special DNA computer was designed to solve the vertex coloring problem. The main body of this kind of DNA computer was polyacrylamide gel electrophoresis which could be classified into three parts: melting region, unsatisfied solution region and solution region. This polyacrylamide gel was con- nected with a controllable temperature device, and the relevant temperature was Tm1, Tm2 and Tm3, res- pectively. Furthermore, with emphasis on the encod- ing way, we succeeded in performing the experiment of a graph with 5 vertices. In this paper we introduce the basic structure, the principle and the method of forming the library DNA sequences.
