Application of fuzzy spiking neural dP systems in energy coordinated control of multi-microgrid

In rewriting P systems, that is P systems using structured strings instead of atomic symbols, rules can be applied in parallel on all strings, but a single rule at a time can be applied on each string. Nonetheless, parallel application of rules also on each string has been considered in various works. This leads to possible application of rules with conflicting target indications on the same string, and different strategies have been considered to face this problem; relations among different classes of languages generated in this way have been investigated in the literature. We continue the investigation on this subject, by highlighting some relations among different classes of maximally parallel rewriting P systems by means of direct simulations. The advantages of such simulations are highlighted, by showing how theoretical results concerning one such type of systems can immediately be adapted to the corresponding simulating systems.

On maximal parallel application of rules in rewriting P systems

Zandron

2023

Implementing perceptrons by means of water-based computing

Civiero,

Henderson,

Hinze

et al. 2024

Water-based computing emerged as a branch of membrane computing in which water tanks act as permeable membranes connected via pipes. Valves residing at the pipes control the flow of water in terms of processing rules. Resulting water tank systems provide a promising platform for exploration and for case studies of information processing by flow of liquid media like water. We first discuss the possibility of realizing a single layer neural network using tanks and pipes systems. Moreover, we discuss the possibility to create a multi-layer neural network, which could be used to solve more complex problems. Two different implementations are considered: in a first solution, the weight values of the connections between the network nodes are represented by tanks. This means that the network diagram includes multiplication structures between the weight tanks and the input tanks. The second solution aims at simplifying the network proposed in the previous implementation, by considering the possibility to modify the weight values associated to neuron by varying the diameter of the connecting pipes between the tanks. The multiplication structures are replaced with a timer that regulates the opening of the outlet valves of all the tanks. These two implementations can be compared to evaluate their efficiency, and considerations will be made regarding the simplicity of implementation.

Medium-long-term electricity load forecasting based on NSNP systems and attention mechanism

Guo,

Wang,

Peng

et al. 2024

Accurate load forecasting can provide important information support for intelligent operation of power systems, it can assist the power grid to deploy production plans in advance to uphold the equilibrium between the supply and demand for electrical power, or plan investment strategies based on the results of the forecast. Nonlinear Spiking Neural P (NSNP) system [1] belongs to a category of computational systems with distributed, parallel and non-deterministic characteristics that have the analytical skill to solve nonlinear problems. Aiming at the temporal characteristics and complex nonlinear characteristics of electrical load data, this paper proposes a new Medium-Long Term Load Forecast model LF-ASNP based on NSNP system and attention mechanism, which can accurately analyze the characteristics of historical load data and forecast the electrical load. In this paper, the LF-ASNP model is validated in several benchmark datasets, and the analysis of the experimental results fully demonstrates that the model can forecast the power load effectively and reliably.