Multiprocessor task assignment with fuzzy Hopfield neural network clustering technique

Most scheduling applications have been demonstrated as NP-complete problems . A variety of schemes are introduced in solving those scheduling applicati ons, such as linear programming, neural networks, and fuzzy logic. In this paper, a new approach of first analogising a scheduling problem to a cluste ring problem and then using a fuzzy Hopfield neural network clustering tech nique to solve the scheduling problem is proposed. This fuzzy Hopfield neur al network algorithm integrates fuzzy c-means clustering strategies into a Hopfield neural network. This investigation utilises this new approach to d emonstrate the feasibility of resolving a multiprocessor scheduling problem with no process migration and constrained times (execution time and deadli ne). Each process is regarded as a data sample, and every processor is take n as a cluster. Simulation results illustrate that imposing the fuzzy Hopfi eld neural network onto the proposed energy function provides an appropriat e approach to solving this class of scheduling problem.