Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

Özbakır, Lale; Delice, Yılmaz

doi:10.1016/j.engappai.2010.11.008

Cited by 20 publications

(4 citation statements)

References 32 publications

(47 reference statements)

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“…Ozbakir et al [25] proposed a novel approach for exploring comprehensible classification rule from trained neural network with Particle Swarm Optimizer (PSO). Neural network is considered as black box, but hybridization with PSO is used to explore the best classification set.…”

Section: Pso In Classificationmentioning

confidence: 99%

Uncertainty Based Hybrid Particle Swarm Optimization Techniques and Their Applications

Anuradha

Tripathy

2015

Multi-Objective Swarm Intelligence

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In order to handle uncertainty in data, several uncertainty based models have been introduced. Perhaps the two most notable models among these are the fuzzy set introduced by Zadeh in 1965 and rough set introduced by Pawlak in 1982. These two models address two different aspects of uncertainty and these are complementary by nature. As a result their hybridization leads to better models. Particle Swarm Optimization (PSO) is an optimization technique that performs optimized search in the solution space for optimization by updation. In this chapter, we discuss on different types of PSOs and their application in classification, feature selection and rule generation. Further, we present several hybridization of PSO with fuzzy approach, rough approach and rough fuzzy approach in developing classification algorithms. Also, we discuss on a dynamic clustering algorithm which uses a rough fuzzy hybrid model embedded with PSO. We provide as an illustration the results of application of this algorithm on several real life data sets and provide its superiority through the computation of several index values for measuring classification accuracy like DB, D, α, ρ, α * and γ . Also, we compile some other applications of PSO.

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Section: Pso In Classificationmentioning

confidence: 99%

Uncertainty Based Hybrid Particle Swarm Optimization Techniques and Their Applications

Anuradha

Tripathy

2015

Multi-Objective Swarm Intelligence

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“…PSO has gained significant attention in the research area of computational intelligence because of its competitive performance, despite its simple implementation. Moreover, it has been applied to solve many real-world engineering design problems, such as power system design (AlRashidi and El-Hawary, 2009;Chen et al, 2007;del Valle et al, 2008;Neyestani et al, 2010;Wang et al, 2013), artificial neural network training (Mirjalili et al, 2012;Yaghini et al, 2013), data clustering (Kiranyaz et al, 2010;Shih, 2006;Sun et al, 2012;Yang et al, 2009), data mining (Özbakır and Delice, 2011;Sarath and Ravi, 2013;Wang et al, 2007), and parameter estimation and system identification (Liu et al, 2008;Modares et al, 2010;Sakthivel et al, 2010), as well as many other engineering problems (Alia and Mandava, 2011;Huang et al, 2009;Lin et al, 2009;Paoli et al, 2009;Sharma et al, 2009).…”

Section: Introductionmentioning

confidence: 98%

Particle swarm optimization with dual-level task allocation

Lim

Isa

2015

Engineering Applications of Artificial Intelligence

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“…Özbakır et al developed the ant colony optimization (ACO) method to obtain meaningful rules from an educated ANN [13]. Özbakır et al proposed a binary PSO method for rule extraction from a trained ANN [14]. Kasiri et al presented a method developed to obtain fuzzy rules from a trained ANN with GA [15].…”

Section: Introductionmentioning

confidence: 99%

Rule extraction and performance estimation by using variable neighborhoodsearch for solar power plant in Konya

Uzun¹,

Özcan²

2020

Turk J Elec Eng & Comp Sci

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The use of renewable energy sources in the production of electricity has become inevitable in order to reduce the greenhouse gases left in the atmosphere that cause the Earth to warm up. Although countries on a national basis have implemented a number of policies to support electricity generated from renewable energy sources, investments to produce electricity without a license on a local basis are not desirable. Those who want to invest medium and small scale for the most reason expect that this work will be supported by real data. Although the electricity generated by renewable investments is generated by simulation data, these data are not realistic for such investors. In this study, the climatic conditions of the power plant of 1 MW installed in Konya and power plant production data are monitored. The artificial neural network (ANN) can achieve a high value for accuracy, but these values are sometimes complex and unclear. In the literature, a number of studies have been conducted using different methods to overcome such problems. Real-time solar power plant (SPP) data were used to determine the feasibility and success of the proposed method. The variable neighborhood search (VNS) metaheuristic method was used to acquire the optimal values belonging to input vectors, G h , which were maximized to the value of the fitness function Fs belonging to output class node s. The results obtained by the VNS method showed that the proposed method has the potential to produce the correct rules. Generally, energy investors are curious about the return on their investment. It is very important for energy providers to estimate how much electricity will be generated from existing solar power plants and accordingly determine the measures they will take to meet the electricity demand in the future. In this study, the performance estimation value obtained from the solar power plant depending on the weather conditions was obtained with 95.55% accuracy.

show abstract

Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

Cited by 20 publications

References 32 publications

Uncertainty Based Hybrid Particle Swarm Optimization Techniques and Their Applications

Uncertainty Based Hybrid Particle Swarm Optimization Techniques and Their Applications

Particle swarm optimization with dual-level task allocation

Rule extraction and performance estimation by using variable neighborhoodsearch for solar power plant in Konya

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