A New Nonlinear Neural Network for Solving QP Problems

Portfolio optimization is frequently used method for the best portfolio selection, according to some objective. Heuristic techniques designed for solving a problem …nding an approximate solution when classic methods fail to …nd any exact solution, have often used in portfolio selection problem. However, almost none of these techniques used a neural network to allocate the proportion of stocks. The main goal of portfolio optimization problem is minimizing the risk of portfolio while maximizing the expected return of the portfolio. This study tackles a neural network in order to solve the portfolio optimization problem. The data set is the daily price of Istanbul Stock Exchange-30 (ISE-30) from May 2015 to May 2017. This study uses Markowitz's Mean-Variance model. Indeed, the portfolio optimization model is quadratic programming (QP) problem. Therefore, many heuristic methods were used to solve portfolio optimization method such as particle swarm optimization, ant colony optimization etc. In fact, these methods do not satisfy stock markets demands in the …nancial world. This study proposed a nonlinear neural network to solve the portfolio optimization problem. In the implementation phase, the proposed method for portfolio optimization problem has more e¤ective results than present methods.

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“…Yan [25] proposed the recurrent neural network for solving quadratic programming problem given in Eq. (6).…”

Section: Nonlinear Neural Networkmentioning

confidence: 99%

“…Moreover, it has faster convergence rate and a more intuitive economic interpretation. In 2014, QP problems can solve with the improved model proposed by Yan [25].…”

Section: Introductionmentioning

confidence: 99%

Portfolio selection based on a nonlinear neural network: An application on the Istanbul Stock Exchange (ISE30)

Yaman

Dalkılıç

2019

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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“…(See the work of Kinderlehrer and Stampacchia 30 ) Theorem 1. The PRNN in (22) is stable in the sense of the Lyapunov stability theory.…”

Section: Lemma 2 Letmentioning

confidence: 99%

“…The closed-loop system, which consists of the system in (1) and the PRNN-based SMC in (22), is stable. Moreover, it is robust against uncertainty d(t) with ||d(t)|| ≤ η, η > 0.…”

Section: Theoremmentioning

confidence: 99%

“…During the last decade, a large number of projection‐based recurrent neural networks are developed for solving various optimization problems. These networks are used for solving linear programming,() QP,() nonlinear programming,() and variational inequality (VI) problems. () The main advantages of these PRNNs for solving the constrained optimization problems are their simple structure and quick convergence time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal sliding‐mode control of linear systems with uncertainties and input constraints using projection neural network

Toshani

Farrokhi

2017

Optim Control Appl Methods

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Summary In this paper, an optimal sliding‐mode control (SMC) method based on the projection recurrent neural networks for a class of linear systems with uncertainties and input constraint is developed. The chattering in the SMC is eliminated by introducing a performance index for minimizing the sliding‐surface variations and the control effort. Moreover, the constraints on the actuators are considered in the optimization problem, which is solved using projection recurrent neural network. The main advantages of the proposed method are obtaining an optimal and chattering‐free control law in a feasible space. Moreover, the parameters of the proposed control method are determined based on the closed‐loop stability and robustness analysis. The performance of the proposed method is evaluated by considering uncertainties and input constraints in the system and is compared with the conventional and a second‐order SMC in the view of chattering, input saturation, and robustness.

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A Nonlinear Neural Network’s Stability Analysis and Its kWTA Application

Yan

2015

Advances in Neural Networks – ISNN 2015

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A New Nonlinear Neural Network for Solving QP Problems

Cited by 9 publications

References 25 publications

Portfolio selection based on a nonlinear neural network: An application on the Istanbul Stock Exchange (ISE30)

Portfolio selection based on a nonlinear neural network: An application on the Istanbul Stock Exchange (ISE30)

Optimal sliding‐mode control of linear systems with uncertainties and input constraints using projection neural network

A Nonlinear Neural Network’s Stability Analysis and Its kWTA Application

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