A novel finite-time q-power recurrent neural network and its application to uncertain portfolio model

Ma, Mingjie; Yang, Jian

doi:10.1016/j.neucom.2021.07.036

Cited by 4 publications

(4 citation statements)

References 26 publications

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Section: Constructing the Optimal Portfoliomentioning

confidence: 99%

“…Some authors add fuzzy neural networks to the market forecasting when conditions change (Ghahtarani, 2021) dramatically. In other recent papers, a finite-time q-power RNN applied to solve the uncertain portfolio model is considered an improvement of classic NN (Ma and Yang, 2021). Another solution to overcome the limitations of traditional and generic portfolio strategies considered in the recent literature is reinforcement learning (RL) using neural networks.…”

Section: Metaheuristics For Portfolio Optimizationmentioning

confidence: 99%

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Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

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Section: Constructing the Optimal Portfoliomentioning

confidence: 99%

Section: Metaheuristics For Portfolio Optimizationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

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“…Recently, some novel representation strategies—such as Lipschitz uncertain state space system (Allahverdi et al, 2020), neural networks (Dong et al, 2021; Ma and Yang, 2021), fuzzy systems (Willian et al, 2021; Zangeneh et al, 2020), fuzzy neural network (Zhou et al, 2020), and block-oriented nonlinear systems (Castro et al, 2017; Degachi et al, 2020; Li et al, 2021; Mohsen and Mohammad, 2019; Saif et al, 2020)—have been proposed to improve or construct the nonlinear system. In the literature (Allahverdi et al, 2020), the issues of sensor fault detection and isolation for a class of Lipschitz uncertain nonlinear system were addressed.…”

Section: Introductionmentioning

confidence: 99%

Modeling and identification for practical nonlinear process using neural fuzzy network–based Hammerstein system

Zheng

Cao

2023

Transactions of the Institute of Measurement and Control

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To address the strong nonlinearity and unknown disturbance in practical nonlinear process, an identification scheme of neural fuzzy network (NFN)–based Hammerstein nonlinear system using multi-signals is developed in this paper. The proposed Hammerstein system has a static nonlinear subsystem approximated by NFN and a dynamic linear subsystem described by autoregressive exogenous system (ARX). First, the nonlinear subsystem and the linear subsystem are separated and identified by the designed multi-signals, and then parameters of the linear subsystem and noise model are identified simultaneously by making use of recursive extended least squares approach, which is effective for compensating the error caused by output noise. Furthermore, in order to cope with unmeasurable variable issue of the identified system, auxiliary model technology is developed, and the nonlinear subsystem parameters are estimated by applying derived auxiliary model recursive extended least squares approach. Experimental results of three typical nonlinear processes verify the effectiveness and accuracy of the proposed method, and the simulation results show that the proposed method can obtain higher identification accuracy than other optimization algorithms.

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“…1–3 Also, the importance of the nonlinear dynamical systems is reflected in model prediction, classification, and decision making and so on. Over the last decades, a wide variety of effective modeling methodologies have been developed for approximating nonlinear systems, such as Volterra series, 4 neural networks, 5,6 support vector machines, 7 fuzzy logic systems, 8 and block-oriented nonlinear systems. 9–13 Among these developed modeling methods, the block-oriented systems have attracted a lot of interest owing to prominent modeling ability.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification for Hammerstein nonlinear system with polynomial and state space model

Cao

2022

Measurement and Control

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This study investigates a two-stage parameter identification algorithm for the Hammerstein nonlinear system based on special test signals. The studied Hammerstein nonlinear system has a static nonlinear subsystem represented by polynomial basis function and a dynamic linear subsystem described by canonical observable state space model, and special test signals composed of binary signals and random signals are applied to parameter identification separation of the nonlinear subsystem and linear subsystem. The detailed identification procedures consist of two main steps. Firstly, using the characteristics that binary signals do not excite the static nonlinear subsystem, the dynamic linear subsystem parameters are identified through recursive least squares algorithm based on input-output data of binary signals. Secondly, unmeasurable state variables of the identified system are replaced with estimated values, thus the nonlinear subsystem parameters are obtained using recursive least squares algorithm with the help of input-output data of random signals. The efficiency and accuracy of proposed identification scheme are confirmed on experiment results of a numerical simulation and a practical nonlinear process, and experimental simulation results show that the developed two-stage identification algorithm has excellent predictive performance for identifying the Hammerstein nonlinear state space systems.

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A novel finite-time q-power recurrent neural network and its application to uncertain portfolio model

Cited by 4 publications

References 26 publications

Enhancing portfolio management using artificial intelligence: literature review

Enhancing portfolio management using artificial intelligence: literature review

Modeling and identification for practical nonlinear process using neural fuzzy network–based Hammerstein system

Parameter identification for Hammerstein nonlinear system with polynomial and state space model

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