Spiking neurons in 3D growing self-organising maps

In Kohonen’s Self-Organising Maps (SOM) learning, preserving the map topology to simulate the actual input features appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. Nonetheless, it is a challenging task as most of the real problems are composed of complex and insufficient data. Spiking Neural Network (SNN) is the third generation of Artificial Neural Network (ANN), in which information can be transferred from one neuron to another using spike, processed, and trigger response as output. This study, hence, embedded spiking neurons for SOM learning in order to enhance the learning process. The proposed method was divided into five main phases. Phase 1 investigated issues related to SOM learning algorithm, while in Phase 2; datasets were collected for analyses carried out in Phase 3, wherein neural coding scheme for data representation process was implemented in the classification task. Next, in Phase 4, the spiking SOM model was designed, developed, and evaluated using classification accuracy rate and quantisation error. The outcomes showed that the proposed model had successfully attained exceptional classification accuracy rate with low quantisation error to preserve the quality of the generated map based on original input data. Lastly, in the final phase, a Spiking 3D Growing SOM is proposed to address the surface reconstruction issue by enhancing the spiking SOM using 3D map structure in SOM algorithm with a growing grid mechanism. The application of spiking neurons to enhance the performance of SOM is relevant in this study due to its ability to spike and to send a reaction when special features are identified based on its learning of the presented datasets. The study outcomes contribute to the enhancement of SOM in learning the patterns of the datasets, as well as in proposing a better tool for data analysis

Spiking Self-organizing Maps for Classification Problem

AbstractIn Self-Organizing Maps (SOM) learning, preserving the map topology to simulate the real input features is one of the most important processes. This is done by training the weight values within the Best Matching Unit (BMU) neighborhood. Improper input feeding will cause failure in identifying the potential BMU which will lead to poor map topology. Many studies have been done to optimize the structure of SOM's topology using Artificial Neural Networks (ANN).Spiking Neural Network (SNN) is the third generation of ANN, where information are transferred from one neuron to other using spikes, and processed to trigger response as an output. Current researches have proven that SNN would be an alternative solution for enhancing ANN learning due to its superiority in capturing the internal relationship of neurons. This paper proposes embedded spiking neurons for Kohonen's Self-organizing Maps (SOM) learning to improve its learning process. The proposed Spiking SOM is divided into four main phases. Phase 1 involves the development of the training sample for SOM learning through neural coding schemes. In Phase 2, the spike values are fed into the training process and potential weights are generated. Phase 3 identifies and labels the outputs from the Spiking SOM classification based on the features and characteristics. Finally, in Phase 4, proposed Spiking SOM model is validated using classification accuracy, error quantization and statistical tests using Pearson correlation. Early experiment is conducted using the 1D coding schemes for transforming dataset into spike times with hexagonal lattice structure of SOM network. Result on cancer dataset shows that the tested model has produced feasible classification accuracy with low quantization error. It shows that the 1D coding is capable in preserving the features in the input neurons

An application barnacles mating optimizer for forecasting of full load electrical power output

The application of meta-heuristic algorithms in addressing numerous real-world problems have been proven to be effective. This application has widespread use in different fields including electrical engineering. In this study, a rather new meta-heuristic algorithm is employed in full load electrical power output forecasting viz. Barnacles Mating Optimizer (BMO). Forecasting of full load electrical power output is critical in maximizing the profit from the provided megawatt hours. For this matter, the simulation involved 4 independent variables which includes ambient temperature, atmospheric pressure, relative humidity and vacuum while the output is the hourly full load electrical power output of the plant. The inputs are fed into the BMO algorithm which acts as a forecasting model. The performance of BMO is later compared against two comparable meta-heuristic algorithms namely Grey Wolf Optimizer (GWO) and Moth-flame Optimizer (MFO). Upon completing the simulation, the produced results showed that the BMO is able to produce significantly lower error rates compared to GWO and MFO

Student classification in adaptive hypermedia learning system using neural network

Conventional hypermedia learning system can pose disorientation and lost in hyperspace problem that will cause learning objectives hard to achieve. Adaptive hypermedia learning system is the solution to overcome this problem by personalizing the learning module presented to the student based on the student knowledge acquisition.This research aims to use neural network to classify the student whether he is advanced, intermediate and beginner student.The classification process is important in adaptive hypermedia learning system in order to provide suitable learning module to each individual student by taking consideration of the studentsí knowledge level, his learning style and his performance as he learn through the system. Data about the student will be collected using implicit and explicit extraction technique. Implicit extraction technique gathers and analyses the studentís behavior captured in the server log while they navigate through the system. Explicit extraction technique on the other hand collects studentís basic information from user registration data. Three classifiers were identified in determining the studentís category.The first classifier determines the student initial status based on data collected from explicit data extraction technique.The second classifier identifies studentís status from implicit data extraction technique by monitoring his behavior while using the system.The third classifier, meanwhile will be executed if the student has finished studying and finished doing the exercises provided in the system. Further, the data collected using both techniques will be integrated to form a user profile that will be used for classification using simple back propagation neural network

Knowledge discovery through supervised kohonen network to identify student’s knowledge level in adaptive hypermedia learning system

SitiMariyamShamsuddin2006_KnowledgeDiscoverythroughSupervisedKohonenNetworkThis paper presents a study on method to identify the students’ characteristics in an adaptive hypermedia learning system. The study involves the use of student profiling techniques to identify the features that may be useful to help the researchers have a better understanding of the student in an adaptive learning environment. We propose a supervised Kohonen network with hexagonal lattice structure to classify the student into 3 categories: beginner, intermediate and advance to represent their knowledge level while using the learning system. An experiment is conducted to see the proposed Kohonen network’s performances compared to the other types of Kohonen networks in term of learning algorithm and map structure. 10-fold cross validation method is used to validate the network performances

Anomaly Detection in Time Series Data Using Spiking Neural Network

One of the crucial issues in anomaly detection problems is identifying abnormal patterns in time series data that contains noise and in unstructured form. In order to deal with this problem, a good detector is needed with a capability to learn the complex features in the datasets and extract useful information to distinguish normal and abnormal patterns in the datasets. This study exploits the features of Spiking Neural Network (SNN) to generate potential neurons through its learning. These neurons will spike whenever it detects abnormal pattern in the data. The proposed method is consisting of three stages: 1) initializing the weight values using rank order method; 2) representing the real input data into spike values using Gaussian Receptive Fields; and 3) identifying the firing nodes that indicate the abnormal data. We applied the proposed technique to selected data with anomalies from time series datasets. Experimental results show that the proposed technique is capable of detecting the anomalies in the datasets with reasonable False Alarm Rate

Computing with spiking neuron networks a review

Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike firing. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models. This paper presents the history of the "spiking neuron", summarizes the most currently-in-use models of neurons and synaptic plasticity, the computational power of SNNs is addressed and the problem of learning in networks of spiking neurons is tackle

Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification

Hybrid technique for Self Organizing Map and Particle Swarm Optimization approach is commonly implemented in clustering area. In this paper, a hybrid approach that is based on Enhanced Self Organizing Map and Particle Swarm Optimization (ESOM/PSO) for classification is proposed. Enhanced Self Organization map which based on Kohonen network structure is to improve the quality of the data classification and labeling. New formulation of hexagonal lattice area is used for the enhancement Self Organizing Map structure. The proposed hybrid ESOM/PSO algorithm uses PSO to evolve the weights for ESOM. The weights are trained by ESOM in the first stage. In the second stage, they are optimized by PSO