Modeling and simulation of combinational CMOS logic circuits by ANFIS

Hayati, Mohsen; Rezaei, Abbas; Seifi, Majid; Naderi, Ali

doi:10.1016/j.mejo.2010.04.009

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…The values in Table A1 were employed to obtain the FIS and the values in Table A2 for its validation. The accuracy of the fuzzy inference systems can be determined using either the mean squared error (MSE) or using the root mean squared error (RMSE), where y i is the actual value andŷ i is the estimated one, as shown in Equation (7). As can be observed in Figure 12, after the second step, the results were more accurate than those obtained with the previous FIS; hence, the adjusted FIS was used to model the behavior of the circuit.…”

Section: Voltage Gain Modeling Resultsmentioning

confidence: 99%

“…They employed an adaptive neuro-fuzzy inference system (ANFIS) to tune the initial zero-order Sugeno FIS. Sahu and Dutta [6] also employed fuzzy logic for the optimization of MOS operational amplifiers, and Hayati et al [7] used a Takagi-Sugeno model and an ANFIS for modeling CMOS logic gates. In other studies, Hostos et al [8] presented a design approach for active analog circuits using genetic algorithms, where the fitness function of the genetic algorithm is implemented by means of a fuzzy inference system; Wang et al [9] designed integrated analog and radio frequency circuits.…”

Section: Literature Reviewmentioning

confidence: 99%

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Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Dieste-Velasco

2021

Mathematics

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Simulation programs are widely used in the design of analog electronic circuits to analyze their behavior and to predict the response of a circuit to variations in the circuit components. A fuzzy inference system (FIS) in combination with these simulation tools can be applied to identify both the main and interaction effects of circuit parameters on the response variables, which can help to optimize them. This paper describes an application of fuzzy inference systems to modeling the behavior of analog electronic circuits for further optimization. First, a Monte Carlo analysis, generated from the tolerances of the circuit components, is performed. Once the Monte Carlo results are obtained for each of the response variables, the fuzzy inference systems are generated and then optimized using a particle swarm optimization (PSO) algorithm. These fuzzy inference systems are used to determine the influence of the circuit components on the response variables and to select them to optimize the amplifier design. The methodology proposed in this study can be used as the basis for optimizing the design of similar analog electronic circuits.

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Section: Voltage Gain Modeling Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Dieste-Velasco

2021

Mathematics

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“…It aims to a regularization of the network architecture based on 2 Advances in Fuzzy Systems learning theory and hyperplane clustering-based techniques [4][5][6][7]. The underlying idea of such techniques received in the literature many acknowledgments as, for example, in the case of theoretical models [8][9][10][11][12][13][14] as well as applications to specific fields [15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: X N ]mentioning

confidence: 99%

A Hierarchical Procedure for the Synthesis of ANFIS Networks

Panella

2012

Advances in Fuzzy Systems

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Adaptive neurofuzzy inference systems (ANFIS) represent an efficient technique for the solution of function approximation problems. When numerical samples are available in this regard, the synthesis of ANFIS networks can be carried out exploiting clustering algorithms. Starting from a hyperplane clustering synthesis in the joint input-output space, a computationally efficient optimization of ANFIS networks is proposed in this paper. It is based on a hierarchical constructive procedure, by which the number of rules is progressively increased and the optimal one is automatically determined on the basis of learning theory in order to maximize the generalization capability of the resulting ANFIS network. Extensive computer simulations prove the validity of the proposed algorithm and show a favorable comparison with other well-established techniques.

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“…Additionally, ANN has the ability to understand, adapt to change is an advantage to the FIS. In-depth study has shown merger between FIS and ANN has been created [10][11][12][13]. Mamdani FIS is one of the neuro-fuzzy that have multiple branches, which includes fuzzy network adjustment control study [14], the controller of fuzzy neurons [15] and neuro-fuzzy function estimate [16].…”

Section: Introductionmentioning

confidence: 99%

Neural-Fuzzy model Based Steel Pipeline Multiple Cracks Classification

Elwalwal

Mahzan

Abdalla

2017

J. Phys.: Conf. Ser.

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Abstract. While pipes are cheaper than other means of transportation, this cost saving comes with a major price: pipes are subject to cracks, corrosion etc., which in turn can cause leakage and environmental damage. In this paper, Neural-Fuzzy model for multiple cracks classification based on Lamb Guide Wave. Simulation results for 42 sample were collected using ANSYS software. The current research object to carry on the numerical simulation and experimental study, aiming at finding an effective way to detection and the localization of cracks and holes defects in the main body of pipeline. Considering the damage form of multiple cracks and holes which may exist in pipeline, to determine the respective position in the steel pipe. In addition, the technique used in this research a guided lamb wave based structural health monitoring method whereas piezoelectric transducers will use as exciting and receiving sensors by Pitch-Catch method. Implementation of simple learning mechanism has been developed specially for the ANN for fuzzy the system represented.

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Modeling and simulation of combinational CMOS logic circuits by ANFIS

Cited by 13 publications

References 12 publications

Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Application of a Fuzzy Inference System for Optimization of an Amplifier Design

A Hierarchical Procedure for the Synthesis of ANFIS Networks

Neural-Fuzzy model Based Steel Pipeline Multiple Cracks Classification

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