Comparison of neuro-fuzzy and neural networks techniques for estimating ammonia concentration in poultry farms

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

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Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature

Applied Soft Computing ◽

10.1016/j.asoc.2020.106275 ◽

2020 ◽

Vol 92 ◽

pp. 106275 ◽

Cited By ~ 6

Author(s):

Paulo Vitor de Campos Souza

Keyword(s):

Neural Networks ◽

Fuzzy Neural Networks ◽

Neuro Fuzzy ◽

Fuzzy Neural

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RAINFALL-RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM MODELS

Uncertainty Modeling in Knowledge Engineering and Decision Making - World Scientific Proceedings Series on Computer Engineering and Information Science ◽

10.1142/9789814417747_0152 ◽

2012 ◽

pp. 951-956 ◽

Cited By ~ 2

Author(s):

GHOLAMALI GHAFARI ◽

MEHDI VAFAKHAH

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Rainfall Runoff ◽

Inference System ◽

System Models ◽

Neuro Fuzzy ◽

Runoff Modeling ◽

Artificial Neural

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Modeling research on the sorption kinetics of pentachlorophenol (PCP) to sediments based on neural networks and neuro-fuzzy systems

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2006.06.016 ◽

2007 ◽

Vol 20 (2) ◽

pp. 239-247 ◽

Cited By ~ 6

Author(s):

Xiao-kang Su ◽

Guang-ming Zeng ◽

Guo-he Huang ◽

Jian-bing Li ◽

Jie Liang ◽

...

Keyword(s):

Neural Networks ◽

Fuzzy Systems ◽

Sorption Kinetics ◽

Neuro Fuzzy ◽

Kinetics Of

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Intelligent Biometric System Using Soft Computing Tools

Biomedical Engineering and Information Systems - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-61692-004-3.ch014 ◽

2011 ◽

pp. 259-276

Author(s):

Anupam Shukla ◽

Ritu Tiwari ◽

Chandra Prakash Rathore

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Soft Computing ◽

Fuzzy System ◽

Person Identification ◽

Security Applications ◽

Neuro Fuzzy ◽

Biometric Systems ◽

Physical Attributes ◽

Novel Concept

Biometric Systems verify the identity of a claimant based on the person’s physical attributes, such as voice, face or fingerprints. Its application areas include security applications, forensic work, law enforcement applications etc. This work presents a novel concept of applying Soft Computing Tools, namely Artificial Neural Networks and Neuro-Fuzzy System, for person identification using speech and facial features. The work is divided in four cases, which are Person Identification using speech biometrics, facial biometrics, fusion of speech and facial biometrics and finally fusion of optimized speech and facial biometrics.

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Neuro-Fuzzy System Modeling

Intelligent Soft Computation and Evolving Data Mining ◽

10.4018/978-1-61520-757-2.ch008 ◽

2010 ◽

pp. 147-175

Author(s):

Chen-Sen Ouyang

Keyword(s):

Neural Networks ◽

Fuzzy System ◽

Fuzzy Systems ◽

System Modeling ◽

Fuzzy Modeling ◽

Structure Identification ◽

Learning Capability ◽

Computing Paradigm ◽

Emerging Trends ◽

Neuro Fuzzy

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.

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Time Series Based House Sale Value Market Forecasting Using Genetically Evolved Neural Networks

Business Intelligence in Economic Forecasting ◽

10.4018/978-1-61520-629-2.ch014 ◽

2010 ◽

pp. 265-282

Author(s):

George S. Atsalakis ◽

Kimon P. Valavanis ◽

Constantin Zopounidis ◽

Dimitris Nezis

Keyword(s):

Neural Networks ◽

Linear Regression ◽

Mean Square Error ◽

Fuzzy Inference ◽

Housing Prices ◽

Individual Investors ◽

Mean Square ◽

Ann Model ◽

Neuro Fuzzy ◽

Proposed Model

Accurate forecasting of the house sale value market is important for individual investors, business investors, banks and mortgage companies. This chapter uses fundamentals of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) to derive and implement a hybrid, genetically evolved feedforward ANN model that predicts next month house sale prices. Derived model results are compared with results obtained using a linear regression model and an Adaptive Neuro Fuzzy Inference System (ANFIS). The proposed model returned lower Root Mean Square Error (RMSE), Absolute Mean Error (MAE), Mean Square Error (MSE) and Mean Absolute Percent Error (MAPE) results compared with the linear regression and ANFIS models. For case studies real monthly data of USA housing prices from 1963 to 2007 were used.

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