An efficient recurrent neural network model for solving fuzzy non-linear programming problems

Mansoori, Amin; Effati, Sohrab; Eshaghnezhad, Mohammad

doi:10.1007/s10489-016-0837-4

Cited by 22 publications

(6 citation statements)

References 39 publications

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“…Example 6 (30). (Manufacturing Systems) A manufacturing factory is going to produce 2 kinds of products A and B in a period (such as one month).…”

Section: F I G U R E 13mentioning

confidence: 99%

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A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

Jahangiri,

Nazemi

2024

Adaptive Control & Signal

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SummaryIn the presented study, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is calculated using a recurrent neural network (RNN) model. Since there is a few research for solving FNLOP by RNN's, we give a new approach to solve the problem. By reducing the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are given. Moreover, we use the KKT conditions into a RNN as an important tool to solve the problem. Besides, the global convergence properties and the Lyapunov stability of the dynamic model are studied in this study. In the final step, some illustrative examples are considered to establish the obtained results. Reported results are compared with some others network models.

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“…Example 6 (30). (Manufacturing Systems) A manufacturing factory is going to produce 2 kinds of products A and B in a period (such as one month).…”

Section: F I G U R E 13mentioning

confidence: 99%

“…The pioneering works on a RNN for constrained programming problem are achieved by Hopfield and Tank 28,29 . There are several popular RNN's for constrained optimization which can be divided into the following two classes: First, the gradient based RNN's in References 30–41. Second, the projection type RNN's in References 42–59.…”

Section: Introductionmentioning

confidence: 99%

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

Jahangiri,

Nazemi

2024

Adaptive Control & Signal

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“…Moreover, the use of IWD algorithm for optimizing fuzzy and recurrent fuzzy system shown in the paper has not been attempted in the past to the best of our knowledge. The optimized fuzzy type‐1 and recurrent fuzzy systems have been used for identification along with controlling of dynamical systems which are non‐linear Mansoori et al (2017). This paper also presents a comparative analysis among four different optimization techniques namely the IWD algorithm, grasshopper algorithm, PSO based algorithm and the gradient descent method.…”

Section: Introductionmentioning

confidence: 99%

Modelling and control of fuzzy‐based systems using intelligent water drop algorithm

et al. 2022

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Identification and controlling of non‐linear and complex dynamical systems have been a major concern and a serious topic of study in the field of adaptive control systems. Various techniques based on artificial intelligence have been used for this purpose. For modelling and control of plants, it is essential to optimize the values of the parameters associated with the respective technique. Searching the right or optimum value of the associated parameters is known as optimization. The intelligent water drop (IWD) algorithm is an optimization algorithm derived from the natural intelligence of water drop, which always chooses the shortest possible path while travelling from rivers and lakes towards oceans and seas. This paper introduces a new way of visualizing and understanding the IWD algorithm and presents its respective application in the field of modelling and controlling of non‐linear dynamic systems with the help of illustrative examples. Fuzzy type‐1 and recurrent fuzzy systems have been optimized using this algorithm. Its learning ability is compared with that of the grasshopper algorithm, a particle swarm optimization (PSO) based algorithm and the gradient descent method. The examples show the superiority of the IWD algorithm over the other optimization techniques.

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“…By applying the RNN, the system can have previous information and hence the network performance can be further improved [20]. In the past decades, many studies have applied the feature of the RNN to their designed network structure [21]- [25]. In 2016, Bao and Zheng [22] proposed a discrete-time recurrent neural network with discontinuous activation functions.…”

Section: Introductionmentioning

confidence: 99%

“…Also in 2016, Lin et al [23] introduced a recurrent fuzzy neural cerebellar model articulation network fault-tolerant control of six-phase permanent magnet synchronous motor position servo drive. In 2017, Mansoori et al [25] provided an efficient recurrent neural network model for solving fuzzy non-linear programming problems.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Recurrent Interval Type-2 Petri Fuzzy Design for Time-Varying Delay Systems

2019

IEEE Access

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This paper presents a design of a self-organizing recurrent interval type-2 Petri fuzzy controller (SORIT2PFC) for controlling the time-varying delay systems. The control system comprises of a main controller and a compensation controller; the SORIT2PFC as the main controller is used to imitate an ideal controller, and the simple fuzzy compensator controller is used to eliminate the residual error. By using a self-organizing algorithm, the structure of the proposed network can automatically achieve optimal construction. Recurrent neural network and fuzzy Petri nets are applied to improve system performances and to reduce computational burden. Online tune adaptive laws of the proposed controller are derived by implementing the gradient descent method and the Lyapunov stability theorem. Finally, the control efficacy and effectiveness of the proposed controller are verified by the numerical simulations of the time-varying delay systems.INDEX TERMS Type-2 fuzzy system, Petri-fuzzy, recurrent neural network, self-organizing algorithm, time-varying delay systems.

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An efficient recurrent neural network model for solving fuzzy non-linear programming problems

Cited by 22 publications

References 39 publications

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

Modelling and control of fuzzy‐based systems using intelligent water drop algorithm

Self-Organizing Recurrent Interval Type-2 Petri Fuzzy Design for Time-Varying Delay Systems

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