Efficient neuro-fuzzy rule generation by parametrized gradient descent for seismic event discrimination

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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Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake

Intelligent Computational Paradigms in Earthquake Engineering ◽

10.4018/978-1-59904-099-8.ch007 ◽

2007 ◽

pp. 123-157 ◽

Cited By ~ 2

Author(s):

Martha Carreño ◽

Omar Cardona ◽

Alex Barbat

Keyword(s):

Decision Making ◽

Fuzzy Sets ◽

Fuzzy System ◽

Fuzzy Rule ◽

Neuro Fuzzy ◽

Computational Intelligence Technique ◽

Artificial Neural ◽

Fuzzy Assessment ◽

Rule Bases ◽

Building Occupancy

This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-fuzzy system, developed with the objective of assisting nonexpert professionals of building construction to evaluate the damage and safety of buildings after strong earthquakes, facilitating decision-making during the emergency response phase on their habitability and reparability. A hybrid neuro-fuzzy system is proposed, based on a special three-layer feedforward artificial neural network and fuzzy rule bases. The inputs to the system are fuzzy sets, taking into account that the damage levels of the structural components are linguistic variables, defined by means of qualifications such as slight, moderate or severe, which are very appropriate to handle subjective and incomplete information. The chapter is a contribution to the understanding of how soft computing applications, such as artificial neural networks and fuzzy sets, can be used to complex and urgent processes of engineering decision-making, like the building occupancy after a seismic disaster.

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