A Layered Spiking Neural System for Classification Problems

Zhang

Mastoi

et al. 2023

ICA

Self Cite

To guarantee their locomotion, biped robots need to walk stably. The latter is achieved by a high performance in joint control. This article addresses this issue by proposing a novel human-simulated fuzzy (HF) membrane control system of the joint angles. The proposed control system, human-simulated fuzzy membrane controller (HFMC), contains several key elements. The first is an HF algorithm based on human-simulated intelligent control (HSIC). This HF algorithm incorporates elements of both multi-mode proportional-derivative (PD) and fuzzy control, aiming at solving the chattering problem of multi-mode switching while improving control accuracy. The second is a membrane architecture that makes use of the natural parallelisation potential of membrane computing to improve the real-time performance of the controller. The proposed HFMC is utilised as the joint controller for a biped robot. Numerical tests in a simulation are carried out with the planar and slope walking of a five-link biped robot, and the effectiveness of the HFMC is verified by comparing and evaluating the results of the designed HFMC, HSIC and PD. Experimental results demonstrate that the proposed HFMC not only retains the advantages of traditional PD control but also improves control accuracy, real-time performance and stability.

Section: Introductionmentioning

confidence: 99%

A human-simulated fuzzy membrane approach for the joint controller of walking biped robots

Zhang

Mastoi

et al. 2023

ICA

Self Cite

“…Regarding the application research of SN P systems, studies have focused on combining SN P systems with algorithms to solve real-world problems, and then they have evaluated the performance of the proposed algorithm with the help of experimental results on data sets and comparisons with other algorithms. SN P systems and numerous universal variants have been realized for applications in different real-world domains, such as performing basic arithmetic operations [ 58 ], simulating Boolean circuits [ 59 ], solving classification problems [ 60 ], fault diagnosis [ 61 , 62 ], recognizing English letters [ 63 ], image processing [ 64 , 65 , 66 ], modeling [ 67 ], and time series prediction [ 68 , 69 , 70 ]. As one of the third-generation of ANNs, SN P systems are considered to have significant development potential.…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes

Zhao

2022

Entropy

Spiking neural P systems (SN P systems for short) realize the high abstraction and simulation of the working mechanism of the human brain, and adopts spikes for information encoding and processing, which are regarded as one of the third-generation neural network models. In the nervous system, the conduction of excitation depends on the presence of membrane potential (also known as the transmembrane potential difference), and the conduction of excitation on neurons is the conduction of action potentials. On the basis of the SN P systems with polarizations, in which the neuron-associated polarization is the trigger condition of the rule, the concept of neuronal membrane potential is introduced into systems. The obtained variant of the SN P system features charge accumulation and computation within neurons in quantity, as well as transmission between neurons. In addition, there are inhibitory synapses between neurons that inhibit excitatory transmission, and as such, synapses cause postsynaptic neurons to generate inhibitory postsynaptic potentials. Therefore, to make the model better fit the biological facts, inhibitory rules and anti-spikes are also adopted to obtain the spiking neural P systems with membrane potentials, inhibitory rules, and anti-spikes (referred to as the MPAIRSN P systems). The Turing universality of the MPAIRSN P systems as number generating and accepting devices is demonstrated. On the basis of the above working mechanism of the system, a small universal MPAIRSN P system with 95 neurons for computing functions is designed. The comparisons with other SN P models conclude that fewer neurons are required by the MPAIRSN P systems to realize universality.

“…The theoretical research of membrane computing is developing rapidly and vigorously, which promotes the design of this model in application [ 10 ]. Membrane system models are widely used in engineering optimization, power system fault diagnosis, ecosystem modeling and other aspects [ 11 , 12 ]. In terms of application, many extended membrane algorithms have been proposed to solve problems and have been optimized in improving the efficiency of the algorithm and reducing the time complexity [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

An improved multi-view attention network inspired by coupled P system for node classification

2022

PLoS ONE

Most of the existing graph embedding methods are used to describe the single view network and solve the single relation in the network. However, the real world is made up of networks with multiple views of complex relationships, and the existing methods can no longer meet the needs of people. To solve this problem, we propose a novel multi-view attention network inspired by coupled P system(MVAN-CP) to deal with node classification. More specifically, we design a multi-view attention network to extract abundant information from multiple views in the network and obtain a learning representation for each view. To enable the views to collaborate, we further apply attention mechanism to facilitate the view fusion process. Taking advantage of the maximum parallelism of P system, the process of learning and fusion will be realized in the coupled P system, which greatly improves the computational efficiency. Experiments on real network data sets indicate that our model is effective.