Fault diagnosis of sensor timely and accurately is very important to improve the reliable operation of systems. In the study, fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine is presented in the paper, where chaos particle swarm optimization is chosen to determine the parameters of SVM. Chaos particle swarm optimization is a kind of improved particle swarm optimization, which can not only avoid the search being trapped in local optimum and but also help to search the optimum quickly by using chaos queues. The wireless sensor is employed as research object, and its four fault types including shock, biasing, short circuit and shifting are applied to test the diagnostic ability of CPSO-SVM compared with other diagnostic methods. The diagnostic results show that CPSO-SVM has higher diagnostic accuracy of wireless sensor than PSO-SVM and BP neural network.

Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

We have constructed a multilayered neural network system that identifies brain dynamics from electroencephalogram (EEG) data by error backpropagation (BP) learning. EEG data in the rest state with closed eyes and open eyes are measured with electrodes that are placed by the international 10-20 system. The brain dynamics are embedded in the neural networks. The developed system discriminates the dynamics of the brain dynamics of the brain activities associated with open eyes from those with closed eyes.

Application of neural networks to brain dynamics identification by EEG

If we can estimate the transition of biomedical system from biomedical signals, it can be very useful in increasing the safety of medical treatment. And so, The examination was performed aiming at estimation of the transition and parameter change of some parametric models such as time-variant linear model and time-variant nonlinear model. In this study, we devised an evaluation index using BP Neural Networks as the technique to detect the transition and the parameter change from the measured data. As results of computer simulations, the availability of system identification of the proposed method was confirmed.

Detection of Transition of Various Time Series Model Using BP Neural Networks

A regression algorithm of quasi-linear model with extreme learning machine (QL-ELM) and its applications for nonlinear system identification are presented. The distinctive feature of the proposed method is that the Quasi-linear model is constructed as a linear ARX model with a complicate nonlinear coefficient. It not only has various linearity properties but also shows some good approximation ability. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square, while the nonlinear part is identified through extreme learning machine. The whole methodology is presented in detail. The effectiveness and accuracy of the proposed method is extensively verified in two nonlinear system identification, including a chemical continuously stirred tank reactor (CSTR) process.

Quasi-Linear Extreme Learning Machine Model Based Nonlinear System Identification

The majority of the analyzed calculi from patients are composed of calcium oxalate (CaOx) monohydrate whewellite (Wh) and CaOx dihydrate wedellite (Wd) The urinary calculi were identified by chemical and morphological analysis based on106 urine samples from human voluntary The Crystalluria made by an optical polarized light microscopy The oxaluria and urinary calcium were determined by conventional volumetric assays The aim of this paper was to develop a simple system to predict and classify the type of crystalluria using Artiﬁcial Neural Networks (ANNs) algorithm

http://www.issr-journals.org/links/papers.php?journal=ijias&application=pdf&article=IJIAS-18-154-06

Using concentrations of calcium and oxalates to predict crystalluria type

System identification of biological signal sources is useful for analyses of biological functions and medical diagnoses. Homeostatic dynamics of circadian rhythms are identified here by using an MA model composed of BP neural networks. Wake-sleep rhythms measured for normal subjects are supplied for the identification. The rhythms are regulated to keep their activity constant by homeostasis, while fluctuating by incessant influences of external forces. The neural networks can acquire the regularity included in the data. The order of the MA neural network model depends on subjects, but the first three orders are dominant, usually in the case of no strong external force. The adaptive change of the homeostatic dynamics can be evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamics are kept in a steady state for ten to 20 days at most. Identified properties reflect each subject, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.

Identification of homeostatic dynamics for a circadian signal source using BP neural networks

Biological oscillatory activity in neural networks has been intensively studied over the past years. Neuronal oscillations are the basis of many different behavioral patterns and sensory mechanism. Understanding the dynamic properties of these mechanisms is useful for analyses of biological functions and medical diagnoses. The dynamic characteristics of wake‐sleep circadian rhythms and ECG’s cardiac cycle data measured for normal subjects are identified here, using MA‐BP neural network model. It was found that dynamics of regular components can be captured by the model. The captured dynamics are kept in a steady state for some periods. The order of the MA neural network was suppressively controlled by the first 2∼3 orders. Hence it may be useful for medical diagnoses of circadian rhythms and heart related diseases.

Identification of biological signal sources for circadian and cardiac cycle rhythms using BP neural networks

The dynamics of some biological signal sources may be identified through measured data. The regulating characteristics of the wake-sleep circadian rhythm is identified here as an example by using BP neural networks. As a result, a MA model of neural networks can acquire the characteristics. The change of wake-sleep period is almost controlled suppressively by the data of preceding three days. The change of the dynamics can be evaluated by internal representation of the network. This method may be useful for medical diagnoses.

System identification of a circadian signal source using BP neural networks

Environmental stimulation, e.g., sound, light and temperature, may produce biological signal sources in the brain, which show autonomous activities without the stimuli. The properties of the source are identified by using BP neural networks. Auditory sources and a circadian source are identified as an example from measured data. This method may be very useful for analyzing brain functions and medical diagnoses.

Identification of biological sources by neural networks

Homeostatic dynamics of wake-sleep circadian rhythm has been identified by BP neural networks through the fluctuation of measured data. The neural networks are considered to have acquired a regular component of the fluctuation which also includes a random component. A method is proposed here to evaluate both components from measured data and applied to analysis of the dynamics represented by the neural networks.

http://ieeexplore.ieee.org/iel5/6513/17403/00804076.pdf

Evaluation of regularity acquired by neural networks for circadian data fluctuation

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