The collected spikes from extracellular recordings usually contain noisy data and outliers,which make it difficult to separate them.A method for spike sorting based on robust clustering is proposed to deal with the problem.The clustering method combines the advantage of fuzzy clustering and robust statistical estimators.The number of clusters is obtained by fuzzy cluster validity.In order to reduce the influence of outliers,the validity index is calculated using the weighting intra-cluster distances.The proposed method is suitable to separate neural spikes in the presence of noisy data and outliers.The experiment on real data shows its performance.
A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehicles among the available vehicles, the initial routing of the selected fleet and the routing optimization. Fuzzy C-means (FCM) can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition. Transiently chaotic neural network (TCNN) combines local search and global search, possessing high search efficiency. It will solve the routes to near optimality. A simple tabu search (TS) procedure can improve the routes to more optimality. The computations on benchmark problems and comparisons with other results in literatures show that the proposed algorithm is a viable and effective approach for CVRP.
