Perbandingan Metode Extreme Learning Machine dan Particle Swarm Optimization Extreme Learning Machine untuk Peramalan Jumlah Penjualan Barang
نویسندگان
چکیده
منابع مشابه
Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier
Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning ...
متن کاملRetracted: Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier
[This retracts the article DOI: 10.1155/2015/418060.].
متن کاملExtreme Learning Machine
Slow speed of feedforward neural networks has been hampering their growth for past decades. Unlike traditional algorithms extreme learning machine (ELM) [5][6] for single hidden layer feedforward network (SLFN) chooses input weight and hidden biases randomly and determines the output weight through linear algebraic manipulations. We propose ELM as an auto associative neural network (AANN) and i...
متن کاملFault Diagnosis of Power Transformers using Kernel based Extreme Learning Machine with Particle Swarm Optimization
To improve the fault diagnosis accuracy for power transformers, this paper presents a kernel based extreme learning machine (KELM) with particle swarm optimization (PSO). The parameters of KELM are optimized by using PSO, and then the optimized KELM is implemented for fault classification of power transformers. To verify its effectiveness, the proposed method was tested on nine benchmark classi...
متن کاملParameters Selection of Kernel Based Extreme Learning Machine Using Particle Swarm Optimization
The generalization performance of kernel based extreme learning machine (KELM) with Gaussian kernel are sensitive to the parameters combination (C, γ). The best generalization performance of KELM with Gaussian kernel is usually achieved in a very narrow range of such combinations. In order to achieve optimal generalization performance, the parameters of KELM with Gaussian kernel were optimized ...
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ژورنال
عنوان ژورنال: Majalah Ilmiah Teknologi Elektro
سال: 2016
ISSN: 2503-2372,1693-2951
DOI: 10.24843/mite.2016.v15i01p15