A Hardware Realization Framework for Fuzzy Inference System Optimization DOI Open Access
Saeid Gorgin, Mohammad Sina Karvandi, Somaye Moghari

et al.

Published: Dec. 20, 2023

The effectiveness of Fuzzy Inference Systems (FISs) in manipulating uncertainty and nonlinearity makes them a subject significant interest for decision-making embedded systems. Accordingly, optimizing FIS hardware improves its performance, efficiency, capabilities, leading to better user experience, increased productivity, cost savings. To be compatible with the limited power budget most systems, this paper presents framework realize ultra-low hardware. It supports optimizations both conventional arithmetic as well MSDF-computing highly consistent MSDF-based sensors. In all processes fuzzification, inference, defuzzification are done on serially coming data bits. demonstrate efficiency proposed framework, we utilized Matlab, Chisel3, Vivado implement it from high-level descriptions synthesis. We also developed Scala library Chisel3 establish connection between these tools, bridging gap, facilitating design space exploration at level. Furthermore, realized an navigation autonomous mobile robots unknown environments. Synthesis results show superiority output our suggested terms resource usage energy consumption compared Matlab HDL code generator output.

Language: Английский

A Hardware Realization Framework for Fuzzy Inference System Optimization DOI Open Access
Saeid Gorgin, Mohammad Sina Karvandi, Somaye Moghari

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(4), P. 690 - 690

Published: Feb. 8, 2024

Fuzzy inference systems (FISs) are a key focus for decision-making in embedded due to their effectiveness managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, capabilities, improving user experience, heightened productivity, cost savings. We propose an ultra-low power framework address constraints systems. supports optimizations conventional arithmetic Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, defuzzification processes occur on serially incoming data bits. To illustrate framework’s we implemented it using MATLAB, Chisel3, Vivado, starting from high-level descriptions progressing synthesis. A Scala library Chisel3 was developed connect these tools seamlessly, facilitating design space exploration at level. applied by realizing autonomous mobile robot navigation unknown environments. The synthesis results highlight superiority of our designs over MATLAB HDL code generator, achieving 43% higher clock frequency, 46% 67% lower resource consumption, respectively.

Language: Английский

Citations

2
A new ensemble intuitionistic fuzzy-deep forecasting model: Consolidation of the IFRFs-bENR with LSTM DOI
Özge Cağcağ Yolcu, Ufuk Yolcu

Information Sciences, Journal Year: 2024, Volume and Issue: 679, P. 121007 - 121007

Published: June 22, 2024

Language: Английский

Citations

2
RVFL-LSTM: A lightweight model with long-short term memory for time series DOI
Qiang Liu, Qin Wang, Xizhao Wang

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: unknown, P. 112896 - 112896

Published: Dec. 1, 2024

Language: Английский

Citations

1
NeuroFuzzyMan: A hybrid neuro-fuzzy BiLSTM stacked ensemble model for financial forecasting and analysis: Dataset case studies on JPMorgan, AMZN and TSLA DOI
Ashkan Safari, Sehraneh Ghaemi

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 266, P. 126037 - 126037

Published: Dec. 11, 2024

Language: Английский

Citations

1
Hesitant Intuitionistic Fuzzy Cognitive Map Based Fuzzy Time Series Forecasting Method DOI

Suraj Prakash Fulara,

Shivani Pant, Manish Pant

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 476 - 485

Published: Jan. 1, 2024

Language: Английский

Citations

0
Balancing Material Supply-Demand with ARIMA and Neural Networks DOI Open Access

Han L,

Zhen Xu, Jin Tan

et al.

International Journal of Simulation Modelling, Journal Year: 2023, Volume and Issue: 22(4), P. 712 - 722

Published: Dec. 1, 2023

This study introduces a hybrid Autoregressive Integrated Moving Average Model-Back Propagation (ARIMA-BP) neural network model to improve the accuracy of production material demand forecasting amid growing market competition and diverse customer requirements.By integrating both linear nonlinear elements, enhances efficiency in planning, inventory optimization, operational cost reduction.It explores novel methods align supply demand, optimizing interplay procurement, product output, management.The study's key contribution is approach that informs balanced strategies, with significant implications for effectiveness competitive advantage manufacturing.

Language: Английский

Citations

1
A Novel Approach for Optimal Cluster Identification and N-Order Hesitation Based Time Series Forecasting DOI
Ankit Dixit, Shikha Jain

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(7)

Published: Sept. 11, 2024

Language: Английский

Citations

0
Forecasting Non-stationary Time Series Using Deep Learning in a Fuzzy Time Series Framework and its Application to Stock Markets DOI Creative Commons

A. J. Saleena,

John C. Jessy,

M. C. Lineesh

et al.

Journal of Advances in Applied & Computational Mathematics, Journal Year: 2024, Volume and Issue: 11, P. 100 - 118

Published: Oct. 9, 2024

Non-stationary time series prediction is challenging due to its dynamic and complex nature. Fuzzy models offer a promising solution for forecasting such data, but key challenge lies in partitioning the universe of discourse, which significantly impacts accuracy. Traditional fuzzy often use equal-length interval partitioning, more suited stationary data limits their adaptability non-stationary series. This paper introduces novel variable-length method designed specifically The developed combines Long Short-Term Memory (LSTM) Autoencoder with K-means clustering, enabling dynamic, data-driven that adapts changing characteristics data. LSTM encodes series, clustered using K-means, intervals are defined based on cluster centers. Furthermore, Variable Length Interval Partitioning-based Time Series model (VLIFTS) by incorporating this concepts Markov chain transition probability matrix. In model, sets viewed as states chain, probabilities used phase. validated stock market indices Nifty 50, NASDAQ, S&P 500, Dow Jones. Stationarity heteroscedasticity tested Augmented Dickey-Fuller (ADF) Levene's tests respectively. Statistical forecast accuracy metrics Root Mean Squared Error (RMSE) Absolute Percent (MAPE) show VLIFTS improves over traditional models. hybrid approach enhances modelling can be applied various problems.

Language: Английский

Citations

0
Probabilistic fuzzy set and particle swarm optimization based computational method for fuzzy time series forecasting DOI Creative Commons
Manish Pant, Sanjay Kumar

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: April 14, 2023

Abstract Computational methods for time series forecasting have always an edge over conventional of due to their easy implementation and prominent characteristics coping with large amount data. Many computational fuzzy (FTS) been developed in past using set, intuitionistic set (IFS), hesitant (HFS) incorporating uncertainty, non-determinism, hesitation forecasting. Since probabilistic (PFS) incorporates both non-probabilistic uncertainties simultaneously, we proposed PFS particle swarm optimization (PSO) based method FTS First, a then it is integrated PSO enhance the accuracy forecasted outputs. Unlike other method, used optimize number partitions length intervals. Three diversified data enrolments University Alabama, market price State Bank India (SBI) share at Bombay stock exchange (BSE) India, death cases COVID-19 are compare performance before after its integration terms root mean square error (RMSE). After PSO, outputs increased significantly found better than many existing methods. Goodness also tested tracking signal Willmott index.

Language: Английский

Citations

0
A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis DOI Creative Commons
Erdinç Yücesoy, Erol Eğrioğlu, Eren Baş

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 8, 2023

Abstract Intuitionistic fuzzy time series methods provide a good alternative to the forecasting problem. It is possible use historical values of as well membership and non-membership obtained for effective factors in improving performance. In this study, high order single variable intuitionistic reduced model first introduced. A new method proposed solution problem which functional structure between information forecast by bagging decision trees based on model. method, c-means clustering used create series. To simpler with Bagging trees, input data from lagged variables, memberships, are subjected dimension reduction principal component analysis. The performance compared popular literature ten different randomly S&P500 stock market. According results analyses, better than both classical some shallow deep neural networks.

Language: Английский

Citations

0