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

Kohonen-swarm algorithm for unstructured data in surface reconstruction

This work introduces a new method for surface reconstruction based on hybrid soft computing techniques: Kohonen Network and Particle Swarm Optimization (PSO). Kohonen network learns the sample data through mapping grid that can grow. The implementation is executed by generating Kohonen mapping framework of the data subsequent to the learning process. Consequently, the learned and well-represented data become the input for surface fitting procedure, and in this study, PSO is proposed to probe the optimum fitting points on the surfaces. The proposed algorithms are applied on different types of curve and surfaces to observe its ability in reconstructing the objects while preserving the original shapes. The experimental results have shown that the proposed algorithm have succeeded in producing the reconstructed surfaces with minimum errors generated

Perbandingan Penggunaan Algoritma Krzyzak dengan Algoritma Rambatan Balik Piawai dalam Domain Peramalan

Artikel ini bertujuan mengkaji prestasi rangkaian neural menggunakan algoritma Krzyzak  dengan algoritma rambatan balik piawai dalam domain peramalan. Kajian dilakukan terhadap data siri masa tak bermusimiaitu pengeluaran hasil kayu balak negara. Ukuran prestasi adalah berdasarkan dua perkara iaitu ketepatan ramalan yang dihasilkan dengan menggunakan ralat purata kuasadua (RMS) dan masa pembelajaran, iaitu masa yang diambil oleh setiap model untuk menumpu. Hasil kajian menunjukkan dengan pemilihan pemalar pembelajaran yang kecil, algoritma Krzyzak adalah lebih baik berbanding algoritma rambatan balik piawai bagi peramalan jangka sederhana dan jangka panjang.

Peramalan siri masa bermusim menggunakan rangkaian neural terhadap nyahmusim data

Rencana ini membincangkan peramalan data siri masa bermusim menggunakan salah satu daripada teknik perhitungan lembut, iaitu rangkaian neural terhadap data yang sudah dinyahmusim bagi melihat prestasi rangkaian terhadap rangkaian. Hasil yang diperoleh menggunakan kedua jenis data ini dibandingkan, dan didapati bahawa keputusan peramalan menggunakan model rangkaian neural terhadap data nyahmusim adalah lebih baik dengan kadar peratusan peramalan adalah tinggi. Bagi tujuan perbandingan, hasil yang diperoleh menggunakan kaedah rangkaian neural dibandingkan dengan hasil peramalan menggunakan model Box-Jenkins

____________________________________________________ FORECASTING TIME SERIES DATA USING HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL

Abstract: In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a new hybrid model by combining a linear and nonlinear model for forecasting time series data. The proposed model (GRANN_ARIMA) integrates nonlinear Grey Relational Artificial Neural Network (GRANN) and linear ARIMA model, combining new features such as multivariate time series data as well as grey relational analysis to select the appropriate inputs and hybridization succession. To validate the performance of the proposed model, small and large scale data sets are used. The forecasting performance was compared with several models, and these include: individual models (ARIMA, Multiple Regression, Grey Relational Artificial Neural Network), several hybrid models (MARMA, MR_ANN, ARIMA_ANN), and Artificial Neural Network (ANN) trained using Levenberg Marquardt algorithm. The experiments have shown that the proposed model has outperformed other models with 99.5 % forecasting accuracy for small-scale data and 99.84 % for large-scale data. The empirical results obtained have proved that the GRANN_ARIMA model can provide a better alternative for time series forecasting due to its promising performance and capability in handling time series data for both small and large scale data

Pemodelan Rangkaian Suap Balik Elman bagi Peramalan Harga Rumah

Artikel ini rnembincangkan satu pendekatan Rangkaian Suap Balik iaitu Rangkaian Elrnan bagi peramalan harga rumah teres di Kuala Lumpur. Rangkaian Elman dengan algoritma pembelajaran rambatan balik dikaji bagi mencerap kelakuan data perumahan untuk mendapatkan satu model peramalan yang baik. Lapan faktor yang mempengaruhi harga rumah dicadangkan dan diolah sebagai input parameter rangkaian suap balik Elman. Berdasarkan kutipan pembelajaran pada rangkaian Elman terhadap 80% data latihan perumahan, didapati bahawa rangkaian ini berjaya menghasilkan keputusan yang lebih baik dengan kadar pengelasan adalah 97.6% dan penghasilan ralat yang kecil iaitu 0.012744 terhadap 20% data ujian perurnahan tahun 1997.

Abstract Determining Financial Indicators with Rough Sets Based Feature Selection Techniques – A Review

Better prediction and classification in determining company’s performance are major concern for practitioners and academic research, due to its importance in giving useful information for stock-holder and its potential investors in making a good decision regarding investment. The firm’s performance can be analyzed based on financial indicators as reported in company’s annual report, balance sheet, and income statement. As a result, many financial indicators or ratios need to be considered for classifying the performance of each firm. Therefore, this study will investigate and identify financial indicators that will give the most significance impact in predicting company’s performance. A hybrid of soft computing and hard computing techniques, i.e., rough set method and statistical approach will be explored for pre-analysis and post-analysis in identifying the most significant indicators for the classification of the company’s performance. This study will also investigate the impact of employing difference indicators in predicting high performance and failure of the firms