An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems

This paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. In this approach, six features of current/voltage signals in a small current grounding system are analyzed by considering transient and steady components, respectively; a fault measure is used to quantify the possibility that a line is faulty; information gain degree is discussed to weight the importance of each of the six features; rough set theory is applied to reduce the features; and finally a fuzzy reasoning spiking neural P system is used to construct fault line detection models. Six cases in a small current grounding system prove the effectiveness of the introduced approach

Multi-behaviors coordination controller design with enzymatic numerical P systems for robots

Membrane computing models are parallel and distributed natural computing models. These models are often referred to as P systems. This paper proposes a novel multi-behaviors coordination controller model using enzymatic numerical P systems for autonomous mobile robots navigation in unknown environments. An environment classifier is constructed to identify different environment patterns in the maze-like environment and the multi-behavior coordination controller is constructed to coordinate the behaviors of the robots in different environments. Eleven sensory prototypes of local environments are presented to design the environment classifier, which needs to memorize only rough information , for solving the problems of poor obstacle clearance and sensor noise. A switching control strategy and multi-behaviors coordinator are developed without detailed environmental knowledge and heavy computation burden, for avoiding the local minimum traps or oscillation problems and adapt to the unknown environments. Also, a serial behaviors control law is constructed on the basis of Lyapunov stability theory aiming at the specialized environment, for realizing stable navigation and avoiding actuator saturation. Moreover, both environment classifier and multi-behavior coordination controller are amenable to the addition of new environment models or new behaviors due to the modularity of the hierarchical architecture of P systems. The simulation of wheeled mobile robots shows the effectiveness of this approach

Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis

This paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference ability of tFRSN P systems from classical reasoning to fuzzy reasoning. Some case studies show the effectiveness of the presented method. We also briefly draw comparisons between the presented method and several main fault diagnosis approaches from the perspectives of knowledge representation and inference process

A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning

To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO

A Complete Arithmetic Calculator Constructed from Spiking Neural P Systems and its Application to Information Fusion

© World Scientific Publishing Company Several variants of spiking neural P systems (SNPS) have been presented in the literature to perform arithmetic operations. However, each of these variants was designed only for one specific arithmetic operation. In this paper, a complete arithmetic calculator implemented by SNPS is proposed. An application of the proposed calculator to information fusion is also proposed. The information fusion is implemented by integrating the following three elements: (1) an addition and subtraction SNPS already reported in the literature; (2) a modified multiplication and division SNPS; (3) a novel storage SNPS, i.e. a method based on SNPS is introduced to calculate basic probability assignment of an event. This is the first attempt to apply arithmetic operation SNPS to fuse multiple information. The effectiveness of the presented general arithmetic SNPS calculator is verified by means of several examples

An Adaptive Optimization Spiking Neural P System for Binary Problems

© 2020 World Scientific Publishing Company. Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem. OSNPS is composed of a family of parallel Spiking Neural P Systems (SNPS) that generate candidate solutions of the binary combinatorial problem and a Guider algorithm that adjusts the spiking probabilities of the neurons of the P systems. Although OSNPS is a pioneering structure in membrane computing optimization, its performance is competitive with that of modern and sophisticated metaheuristics for the knapsack problem only in low dimensional cases. In order to overcome the limitations of OSNPS, this paper proposes a novel Dynamic Guider algorithm which employs an adaptive learning and a diversity-based adaptation to control its moving operators. The resulting novel membrane computing model for optimization is here named Adaptive Optimization Spiking Neural P System (AOSNPS). Numerical result shows that the proposed approach is effective to solve the 0/1 knapsack problems and outperforms multiple various algorithms proposed in the literature to solve the same class of problems even for a large number of items (high dimensionality). Furthermore, case studies show that a AOSNPS is effective in fault sections estimation of power systems in different types of fault cases: including a single fault, multiple faults and multiple faults with incomplete and uncertain information in the IEEE 39 bus system and IEEE 118 bus system

Load Estimation of Complex Power Networks from Transformer Measurements and Forecasted Loads

This paper presents a load estimation method applicable to complex power networks (namely, heavily meshed secondary networks) based on available network transformer measurements. The method consists of three steps: network reduction, load forecasting, and state estimation. The network is first mathematically reduced to the terminals of loads and measurement points. A load forecasting approach based on temperature is proposed to solve the network unobservability. The relationship between outdoor temperature and power consumption is studied. A power-temperature curve, a nonlinear function, is obtained to forecast loads as the temperature varies. An “effective temperature” reflecting complex weather conditions (sun irradiation, humidity, rain, etc.) is introduced to properly consider the effect on the power consumption of cooling and heating devices. State estimation is adopted to compute loads using network transformer measurements and forecasted loads. Experiments conducted on a real secondary network in New York City with 1040 buses verify the effectiveness of the proposed method

An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems

Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system. © World Scientific Publishing Company

On Applications of Spiking Neural P Systems

Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations

Automatic Design of Spiking Neural P Systems Based on Genetic Algorithms

At present, all known spiking neural P systems (SN P systems) are established by manual design rather than automatic design. The method of manual design poses two problems: consuming a lot of computing time and making unnecessary mistakes. In this paper, we propose an automatic design approach for SN P systems by genetic algorithms. More specifically, the regular expressions are changed to achieve the automatic design of SN P systems. In this method, the number of neurons in system, the synapse connections between neurons, the number of rules within each neuron and the number of spikes within each neuron are known. A population of SN P systems is created by generating randomly accepted regular expressions. A genetic algorithm is applied to evolve a population of SN P systems toward a successful SN P systems with high accuracy and sensitivity for carrying out specific task. An effective fitness function is designed to evaluate each candidate SN P system. In addition, the elitism, crossover and mutation are also designed. Finally, experimental results show that the approach can successfully accomplish the automatic design of SN P systems for generating natural numbers and even natural numbers by using the .NET framework