Bi-simulation Between P Colonies and P Systems with Multi-stable Catalysts

International audienc

Generalized P Colony Automata and Their Relation to P Automata

We investigate genPCol automata with input mappings that can be realized through the application of finite transducers to the string representations of multisets. We show that using unrestricted programs, these automata characterize the class of recursively enumerable languages. The same holds for systems with all-tape programs, having capacity at least two. In the case of systems with com-tape programs, we show that they characterize language classes which are closely related to those characterized by variants of P automata

A Kernel-Based Membrane Clustering Algorithm

The existing membrane clustering algorithms may fail to handle the data sets with non-spherical cluster boundaries. To overcome the shortcoming, this paper introduces kernel methods into membrane clustering algorithms and proposes a kernel-based membrane clustering algorithm, KMCA. By using non-linear kernel function, samples in original data space are mapped to data points in a high-dimension feature space, and the data points are clustered by membrane clustering algorithms. Therefore, a data clustering problem is formalized as a kernel clustering problem. In KMCA algorithm, a tissue-like P system is designed to determine the optimal cluster centers for the kernel clustering problem. Due to the use of non-linear kernel function, the proposed KMCA algorithm can well deal with the data sets with non-spherical cluster boundaries. The proposed KMCA algorithm is evaluated on nine benchmark data sets and is compared with four existing clustering algorithms