Training ANFIS Parameters with a Quantum-behaved Particle Swarm Optimization Algorithm

Lin, Xiufang; Sun, Jun; Palade, Vasile; Fang, Wei; Wu, Xiaojun; Xu, Wenbo

doi:10.1007/978-3-642-30976-2_18

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…Based on the PSO, Lin et al [28] proposed an approach for training ANFIS parameters. The system focused on utilizing quantum behaving particle swarm optimization (QPSO) for preparing an ANFIS's parameter settings.…”

Section: A Anfis Optimizationmentioning

confidence: 99%

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Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

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Throughout recent years, the progress of telemonitoring and telediagnostics devices for evaluating and tracking Parkinson's (PD) disease has become increasingly important. The early detection of PD increases the consistency of the treatment of patients and ultimately allows it possible to achieve a rapid diagnostic decision from an experienced clinician. In this paper, a proposed fog-based ANFIS+PSOGWO model provided for Parkinson's disease prediction. The proposed model exploits the advantages of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) for adjusting the adaptive neuro-fuzzy inference system (ANFIS) parameters with the use of chaotic tent map for the initialization. The fog processing utilized for gathering and analyzing the data at the edge of the gateways and notifying the local community instantly. Compared to other optimization methods, many evaluation metrics used like the root mean square error (RMSE), the mean square error (MSE), the standard deviation (SD), and the accuracy and five standard datasets from repository of UCI machine learning that demonstrated the superiority of the model proposed against the grey wolf optimization (GWO), the particle swarm optimization (PSO), the differential evolution (DE), the genetic algorithm (GA), the ant colony optimization (ACO), and the standard ANFIS model. Moreover, the proposed ANFIS+PSOGWO applied for Parkinson's disease prediction and achieved an accuracy of 87.5%. The proposed ANFIS+PSOGWO compared in producing positive outcomes better than PSO, GWO, GA, ACO, DE, and some recent literature for Parkinson's disease prediction. The proposed model produced accuracy for the Parkinson's disease prediction has outperformed its closest competitors in all algorithms by 7.3%.

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Section: A Anfis Optimizationmentioning

confidence: 99%

“…The fitness identified as a mean squared error (MSE) from the target amount to the real performance, may have been represented as: (28) where MSE denote mean square error, is the target output, is the average output, and m represent the size of the dataset.…”

Section: Anfis Adaptation Using the Psogwo Algorithmmentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

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“…Several types of integrated approaches have been proposed. Examples of such combinations include neuro‐symbolic approaches integrating neural networks with symbolic methods (Hatzilygeroudis and Prentzas, ; Garcez and Lamb, ), neuro‐fuzzy approaches integrating neural networks with fuzzy methods (Lin et al, ; Chattopadhyay, ), approaches combining neural networks and genetic algorithms (Belciug and Gorunescu, ) and approaches combining case‐based reasoning with other intelligent methods (Prentzas and Hatzilygeroudis, ).…”

Section: Introductionmentioning

confidence: 99%

Assessment of life insurance applications: an approach integrating neuro‐symbolic rule‐based with case‐based reasoning

Prentzas

Hatzilygeroudis

2015

Expert Systems

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Assessment of applications for life insurance is an important task in the insurance sector that concerns estimation of potential risks underlying an application, if accepted. This task is accomplished by specialized personnel of insurance companies. Because of recent financial crises, this task is more demanding, and intelligent computer‐based methods could be employed to assist. In this paper, we present an intelligent approach to assessment of life insurance applications, which is based on an integration of neurule‐based with case‐based reasoning. Neurules are a type of neuro‐symbolic rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. A characteristic of neurules is that in contrast to other hybrid neuro‐symbolic approaches, they retain the naturalness and modularity of symbolic rules. Neurules are produced from available symbolic rules that represent general knowledge, which however do not completely cover the domain. We use health condition, age, gender, annual income, profession, insurance type and primary life insurance benefit as assessment parameters used in rule conditions. The integration of neurules and cases employs different types of indices for the cases according to different roles they play in neurule‐based reasoning. This results in its accuracy improvement. Experimental results demonstrate the effectiveness of the approach.

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“…Some types of combinations have been extensively used and explored. Examples of such combinations, among others, involve neuro-symbolic approaches integrating neural networks with symbolic methods (Hatzilygeroudis & Prentzas 2004;Garcez d'Avila & Lamb, 2011), neuro-fuzzy approaches integrating neural networks with fuzzy methods (Lin et al, 2012;Sreekantha & Kulkarni, 2012;Chattopadhyay, 2014), approaches combining neural networks and genetic algorithms (Belciug & Gorunescu, 2013) and approaches combining case-based reasoning with rule-based reasoning (Prentzas & Hatzilygeroudis, 2007) or other intelligent methods (Prentzas & Hatzilygeroudis, 2009;Chuang & Huang, 2011).…”

Section: Introductionmentioning

confidence: 99%

Improving efficiency of merging symbolic rules into integrated rules: splitting methods and mergability criteria

Prentzas

Hatzilygeroudis

2014

Expert Systems

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Neurules are a type of neuro-symbolic rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Neurules exhibit characteristics such as modularity, naturalness and ability to perform interactive and integrated inferences and provide explanations for reached conclusions. One way of producing a neurule base is through conversion of an existing symbolic rule base yielding an equivalent but more compact rule base. The conversion process merges symbolic rules having the same conclusion into one or more neurules. Because of the inability of the adaline unit to handle inseparability, more than one neurule for each conclusion may be produced by splitting the initial set of symbolic rules into subsets. This paper presents research work improving the conversion process in terms of runtime and number of produced neurules. First, we show how easier it is to construct a neurule base than a connectionist one. Second, we present alternative rule set splitting methods. Finally, we define criteria concerning the ability or inability to convert a rule set into a single, equivalent, but more compact rule. With application of such mergability criteria, the conversion process of symbolic rules into neurules becomes more time-efficient. All the aforementioned are supported by experimental results.

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Training ANFIS Parameters with a Quantum-behaved Particle Swarm Optimization Algorithm

Cited by 11 publications

References 6 publications

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Assessment of life insurance applications: an approach integrating neuro‐symbolic rule‐based with case‐based reasoning

Improving efficiency of merging symbolic rules into integrated rules: splitting methods and mergability criteria

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