3,426 research outputs found
Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration
To reveal fault propagation paths is one of the most critical studies for the analysis of
power system security; however, it is rather dif cult. This paper proposes a new framework for the fault
propagation path modeling method of power systems based on membrane computing.We rst model the fault
propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter
concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models
and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability
and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP
system and make them suitable for large-scale power systems, we propose a model reduction method
for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output
neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE
14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the
SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise
investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction,
and the simulation results show a new success and promising approach to the engineering domain
Application of Neural-Like P Systems With State Values for Power Coordination of Photovoltaic/Battery Microgrids
The power coordination control of a photovoltaic/battery microgrid is performed with a novel
bio-computing model within the framework of membrane computing. First, a neural-like P system with
state values (SVNPS) is proposed for describing complex logical relationships between different modes
of Photovoltaic (PV) units and energy storage units. After comparing the objects in the neurons with the
thresholds, state values will be obtained to determine the con guration of the SVNPS. Considering the
characteristics of PV/battery microgrids, an operation control strategy based on bus voltages of the point of
common coupling and charging/discharging statuses of batteries is proposed. At rst, the SVNPS is used to
construct the complicated unit working modes; each unit of the microgrid can adjust the operation modes
automatically. After that, the output power of each unit is reasonably coordinated to ensure the operation
stability of the microgrid. Finally, a PV/battery microgrid, including two PV units, one storage unit, and
some loads are taken into consideration, and experimental results show the feasibility and effectiveness of
the proposed control strategy and the SVNPS-based power coordination control models
A Kernel-Based Membrane Clustering Algorithm
The existing membrane clustering algorithms may fail to
handle the data sets with non-spherical cluster boundaries. To overcome
the shortcoming, this paper introduces kernel methods into membrane
clustering algorithms and proposes a kernel-based membrane clustering
algorithm, KMCA. By using non-linear kernel function, samples in
original data space are mapped to data points in a high-dimension feature
space, and the data points are clustered by membrane clustering
algorithms. Therefore, a data clustering problem is formalized as a kernel
clustering problem. In KMCA algorithm, a tissue-like P system is
designed to determine the optimal cluster centers for the kernel clustering
problem. Due to the use of non-linear kernel function, the proposed
KMCA algorithm can well deal with the data sets with non-spherical
cluster boundaries. The proposed KMCA algorithm is evaluated on nine
benchmark data sets and is compared with four existing clustering algorithms
Fuzzy reasoning spiking neural P system for fault diagnosis
Spiking neural P systems (SN P systems) have been well established as a novel class of distributed
parallel computing models. Some features that SN P systems possess are attractive
to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required
for many fault diagnosis applications. The lack of ability is a major problem of existing SN P
systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by
introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing
mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems).
The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis
knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning
algorithm based on FRSN P systems is developed according to neuron’s dynamic firing mechanism.
Besides, a practical example of transformer fault diagnosis is used to demonstrate the
feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
Brain image clustering by wavelet energy and CBSSO optimization algorithm
Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights.
The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes
Asynchronous Spiking Neural P Systems with Multiple Channels and Symbols
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computation systems, inspired from the way that the neurons process and communicate information by means of spikes. A new variant of SNP systems, which works in asynchronous mode, asynchronous spiking neural P systems with multiple channels and symbols (ASNP-MCS systems, in short), is investigated in this paper. There are two interesting features in ASNP-MCS systems: multiple channels and multiple symbols. That is, every neuron has more than one synaptic channels to connect its subsequent neurons, and every neuron can deal with more than one type of spikes. The variant works in asynchronous mode: in every step, each neuron can be free to fire or not when its rules can be applied. The computational completeness of ASNP-MCS systems is investigated. It is proved that ASNP-MCS systems as number generating and accepting devices are Turing universal. Moreover, we obtain a small universal function computing device that is an ASNP-MCS system with 67 neurons. Specially, a new idea that can solve ``block'' problems is proposed in INPUT modules
An unsupervised learning algorithm for membrane computing
This paper focuses on the unsupervised learning problem within membrane computing,
and proposes an innovative solution inspired by membrane computing techniques, the
fuzzy membrane clustering algorithm. An evolution–communication P system with nested
membrane structure is the core component of the algorithm. The feasible cluster centers
are represented by means of objects, and three types of membranes are considered: evolution,
local store, and global store. Based on the designed membrane structure and the
inherent communication mechanism, a modified differential evolution mechanism is
developed to evolve the objects in the system. Under the control of the evolution–communication
mechanism of the P system, the proposed fuzzy clustering algorithm achieves
good fuzzy partitioning for a data set. The proposed fuzzy clustering algorithm is compared
to three recently-developed and two classical clustering algorithms for five artificial and
five real-life data sets.National Natural Science Foundation of China No 61170030National Natural Science Foundation of China No 61472328Chunhui Project Foundation of the Education Department of China No. Z2012025Chunhui Project Foundation of the Education Department of China No. Z2012031Sichuan Key Technology Research and Development Program No. 2013GZX015
Dynamic threshold neural P systems
Pulse coupled neural networks (PCNN, for short) are models abstracting the synchronization behavior
observed experimentally for the cortical neurons in the visual cortex of a cat’s brain, and the intersecting
cortical model is a simplified version of the PCNN model. Membrane computing (MC) is a kind computation
paradigm abstracted from the structure and functioning of biological cells that provide models
working in cell-like mode, neural-like mode and tissue-like mode. Inspired from intersecting cortical
model, this paper proposes a new kind of neural-like P systems, called dynamic threshold neural P systems
(for short, DTNP systems). DTNP systems can be represented as a directed graph, where nodes are dynamic
threshold neurons while arcs denote synaptic connections of these neurons. DTNP systems provide a
kind of parallel computing models, they have two data units (feeding input unit and dynamic threshold
unit) and the neuron firing mechanism is implemented by using a dynamic threshold mechanism. The
Turing universality of DTNP systems as number accepting/generating devices is established. In addition,
an universal DTNP system having 109 neurons for computing functions is constructed.National Natural Science Foundation of China No 61472328Research Fund of Sichuan Science and Technology Project No. 2018JY0083Chunhui Project Foundation of the Education Department of China No. Z2016143Chunhui Project Foundation of the Education Department of China No. Z2016148Research Foundation of the Education Department of Sichuan province No. 17TD003
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