As a novel computational model, spiking neural P system (SNP system) has been shown to have great computational power and potential. In this article puts forward a new kind of variant SNP system, that is, spiking neural P systems with anti-spikes and autapses (ASNP-AU systems). Autapses are the synapses where a neuron connects to itself, which transmit the spike sent by the neuron back to itself. Various types of rules in neurons are involved with anti-spikes. Spikes and anti-spikes meet in a neuron will cancel each other. The system is also designed to exclude delay feature in neurons. By simulating the register machine, it can be shown that this system can generate/accept Turing-computable numbers with reduced use of rules or neurons.
The minimum viable population (MVP) refers to the smallest number of individuals in a population required to ensure its long-term survival in a specific environment. In this paper, a multi-environment population dynamics P system (MPDP system) is introduced to estimate the MVP of giant pandas released into the wild from the Chengdu Research Base of Giant Panda Breeding (GPBB) and the China Conservation and Research Center for the Giant Panda (CCRCGP). The MPDP system takes into account disease and natural disasters. The parameters birth rate, mortality rate and population size are also discussed. Simulation experiments show that the MPDP system yields better results than the four models found in the literature.
Artificial neural networks of first generation have dominated the area of learning systems with many state-of-the-art methods on tasks that were previously thought to be solvable only by humans. Among these types of neural networks, one of the simplest type is that of a Multi-Layer Perceptron (MLP). Spiking Neural P systems (SN P systems) are another class of artificial neural networks of third generation that are inspired by the cell membrane mechanisms and how they communicate. We propose in this paper a new type of SN P system, called Learning SN P system with Error Propagation (LSNPEP system), with an architecture similar to that of a Layered Spiking Neural P system (LSN P system). The LSNPEP system uses the membrane computing mechanisms and their rules to learn in a way which is similar to MLPs and therefore, showing that LSNPEP systems can learn anything MLPs can do. In this paper we describe the LSNPEP system showing its similarities, but also its main extensions making it slightly more general than LSN P system, proving its superiority in terms of accuracy (on the testing side) not only on LSN P system models, but on five more other approaches on eight different classification datasets including tabular data and images. It surpasses by a large margin the LSN P system on the digit recognition dataset.
A recent variant of spiking neural P systems (SN P systems) are numerical SN P systems (NSN P systems). NSN P systems combine the spiking and neuron architecture of SN P systems and the computational processes of numerical P systems. Multiple attempts to simulate and visualize SN P Systems are already present such as the WebSnapse tool. Given that NSN P systems is a recent variant there is still a lack of Simulation and Visualization (S&V) tools for NSN P systems. We introduce Numerical WebSnapse: a tool intended for simulating and visualizing NSN P systems. The Numerical WebSnapse tool is based on a hybrid S&V architecture. Distributing processing tasks between the client and server improves performance and provides modularity, to facilitate scalability and flexibility with module implementations. We discuss the design and architecture of Numerical WebSnapse, how it improves on previous S&V tools for SN P sytems. Numerical WebSnapse is tested on various (non)deterministic NSN P systems from literature, including a stress testing for the tool.
A fuzzy reasoning spiking neural P system (FRSNPS) offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description of the relationships between protective devices and faults, and an understandable diagnosis model-building process. In this paper, the first attempt is made to extend FRSNPS from an electrical system to an electromechanical system and correspondingly, a requesting numerical spiking neural P system and a fuzzy reasoning numerical spiking neural P system (FRNSNPS) are proposed to diagnose faults in the lubrication system of aviation piston engines. This paper first constructs a simulation model for the lubrication system based on its working principles and the measurement data of the specific engine, and verifies its correctness. A fault dataset of the lubrication system is generated considering three typical fault types. Next, a fault diagnosis method for the lubrication system of an aviation piston engine is designed. The method utilized comprises the construction of a bond graph model of the lubrication system, the derivation of a temporal causal graph and the use of forward reasoning to reveal the causal relationship between different fault types and the changes of conditions in the lubrication system. Subsequently, a diagnosis model for the lubrication system is built with an FRNSNPS. Simulation experiments show that the proposed method can correctly diagnose three typical fault types in the lubrication system under two cruising conditions.
