In this paper, intuitionistic fuzzy spiking neural P (IFSNP) systems as a variant are proposed by integrating intuitionistic fuzzy logic into original spiking neural P systems. Compared with a common fuzzy set, intuitionistic fuzzy set can more finely describe the uncertainty due to its membership and non-membership degrees. Therefore, IFSNP systems are very suitable to deal with fault diagnosis of power systems, specially with incomplete and uncertain alarm messages. The fault modeling method and fuzzy reasoning algorithm based on IFSNP systems are discussed. Two examples are used to demonstrate the availability and effectiveness of IFSNP systems for fault diagnosis of power systems. Case studies involve single fault, complex fault, and multiple faults with protection device failures and incorrect tripping signals.

Inspired by Eckhorn's neuron model that emulates a mammal's visual cortex, this paper proposes a new kind of neural-like P system, called a coupled neural P (CNP) system. The CNP system consists of some coupled neurons, each with three components: receptive field, modulation, and output module. CNP systems are a kind of distributed parallel-computing model with a directed graph structure like spiking neural P systems. Moreover, CNP systems have a nonlinear coupled-modulation characteristic and a dynamic threshold mechanism. The computational power of CNP systems is discussed. Specifically, it is proved that CNP systems as number-generating devices are Turing universal. Moreover, we provide a small universal CNP system for function-computing devices.

Spiking neural P (SNP) systems are a class of neural-like computing models, abstracted by the mechanism of spiking neurons. This article proposes a new variant of SNP systems, called gated spiking neural P (GSNP) systems, which are composed of gated neurons. Two gated mechanisms are introduced in the nonlinear spiking mechanism of GSNP systems, consisting of a reset gate and a consumption gate. The two gates are used to control the updating of states in neurons. Based on gated neurons, a prediction model for time series is developed, known as the GSNP model. Several benchmark univariate and multivariate time series are used to evaluate the proposed GSNP model and to compare several state-of-the-art prediction models. The comparison results demonstrate the availability and effectiveness of GSNP for time series forecasting.