USNFIS: Uniform stable neuro fuzzy inference system

Rubio, José de Jesús

doi:10.1016/j.neucom.2016.08.150

Cited by 53 publications

(10 citation statements)

References 49 publications

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“…A description of methods employing different evolutionary algorithms in [30][31][32][33][34][35]. Some recent new version of the ANFIS are presented in [36][37][38][39]. An approach is proposed in [40,41] against the mean-variance method for portfolio selection problem named as full-scale optimisation.…”

Section: Related Workmentioning

confidence: 99%

“…The output parameter obtained from this structure of ANFIS is a kind of benchmark for the performance of the proposed framework. The basic model of ANFIS is described in [17] and the recent new version of the ANFIS is given in [36][37][38][39]. A modified model is presented in this paper that has a new structure having six layers.…”

Section: Phase 2 Design Of New Six-layered Structure Of Anfismentioning

confidence: 99%

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A Novel Framework for Portfolio Selection Model Using Modified ANFIS and Fuzzy Sets

Kumar

Doja

2018

Computers

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This paper proposes a novel framework for solving the portfolio selection problem. This framework is excogitated using two newly parameters obtained from an existing basic mean variance model. The scheme can prove entirely advantageous for decision-making while using computed values of these significant parameters. The framework combines effectiveness of the mean-variance model and another significant parameter called Conditional-Value-at-Risk (CVaR). It focuses on extracting two newly parameters viz. αnew and βnew, which are demarcated from results obtained from mean-variance model and the value of CVaR. The method intends to minimize the overall cost, which is computed in the framework using quadratic equations involving these newly parameters. The new structure of ANFIS is designed by changing existing structure of ANFIS and this new structure contains six layers instead of existing five-layered structure. Fuzzy sets are harnessed for the design of the second layer of this new ANFIS structure. The output parameter acquired from the sixth layer of the new ANFIS structure serves as an important index for an investor in the decision-making. The numerical results acquired from the framework and the new six-layered structure is presented and these results are assimilated and compared with the results of the existing ANFIS structure.

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Section: Related Workmentioning

confidence: 99%

Section: Phase 2 Design Of New Six-layered Structure Of Anfismentioning

confidence: 99%

A Novel Framework for Portfolio Selection Model Using Modified ANFIS and Fuzzy Sets

Kumar

Doja

2018

Computers

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“…The basic FIS system (Zadeh, 2015;Grande et al, 2017;Paramo-Carranza et al, 2017;Rubio, 2017) is composed as shown in Figure 3.…”

Section: Dsm With Fismentioning

confidence: 99%

Modeling Uncertainty for the Double Standard Model Using a Fuzzy Inference System

2018

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This paper studies the issue of uncertainty in the ambulance location problem to cover the maximum number of demand points in a city. The work is based on the double standard model (DSM), a popular coverage model where two radii are considered to cover a percentage of the demand points twice. Uncertainty is introduced in the expected travel time between an ambulance and a demand point, before computing the optimal placement of ambulances in potential bases by solving the linear program posed by the DSM. The following three approaches are considered: (1) solving the DSM without uncertainty; (2) uncertainty in the travel time is based on triangular fuzzy set; and (3) a fuzzy inference system (FIS) with a rule base derived from the problem properties, which is the main contribution of this work. Results show that considering uncertainty can have a significant effect on the solutions for the DSM, with the solutions produced with the FIS approach achieving a higher total coverage of the demand. In conclusion, the proposed strategy could provide a reliable and effective tool to support decision making in the ambulance location problem by considering uncertainty in the ambulance travel times.Keywords: ambulances, emergency medical services, bases, double standard model, triangular fuzzy set, fuzzy inference system inTrODUcTiOn In recent years, the development of computational support systems for emergency medical services (EMS) has attracted a growing amount of attention from researchers. In EMSs, a crucial factor that must be considered is response time. For example, let us consider cardiac arrest, the American Heart Association concluded that in the first 4-6 min after a person suffers a cardiac arrest she (he) can begin to suffer from permanent brain damage or even brain death. However, normal heart rhythm can be restored if advanced life support (ALS) is provided early. Studies have shown that the probability of a patient surviving is reduced by 7-10% with each passing minute in which defibrillation and ALS are not provided, and resuscitation is mostly unsuccessful after 10 min (EMSWorld.com, 2014).Another example is traffic accident, where the number of deaths was estimated to be 1.25 million in 2013. Half of all fatalities caused by traffic accidents are pedestrians, cyclists, and motorcyclists. Death rates from such accidents are considerably higher in developing countries (OMG, 2015). Therefore, appropriate and timely response times to such incidents are indispensable in highly urban areas.In 2013, the Red Cross of Tijuana (RCT) covered about 98% of the EMS requests (Cruz Roja de Tijuana, 2012), providing medical attention to 37,000 emergency calls. It does this with 13 ambulances that are distributed in 8 bases that cover a population of approximately 1.6 million people in an area of 1,243 km 2 (INEGI, 2010). This means that, on average, each ambulance serves about 123,000 people and each base must cover an average of 155 km 2 . This contrasts, for instance, with the US, where by the 1990s there was about 1 am...

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“…[14,15]. A basic architecture of RBFNN network is shown in Figure 2, which consists of three layers, namely, input layer, hidden layer, and output layer.…”

Section: Preliminaries Of Neural Networkmentioning

confidence: 99%

“…This enables us to deal with control problems for complex nonlinear systems [8][9][10][11][12][13]. In addition to system modeling and control, NN has also been successfully applied in various fields such as learning [14][15][16][17], pattern recognition [18], and signal processing [19]. And NN has been extensively used for functions approximation, such as to compensate for the effect of unknown dynamics in nonlinear systems [20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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USNFIS: Uniform stable neuro fuzzy inference system

Cited by 53 publications

References 49 publications

A Novel Framework for Portfolio Selection Model Using Modified ANFIS and Fuzzy Sets

A Novel Framework for Portfolio Selection Model Using Modified ANFIS and Fuzzy Sets

Modeling Uncertainty for the Double Standard Model Using a Fuzzy Inference System

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

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