A Neural Network Approach to Electromyographic Signal Processing for a Motor Control Task

1997 ◽  
Vol 119 (2) ◽  
pp. 335-337 ◽  
Author(s):  
W. T. Lester ◽  
R. V. Gonzalez ◽  
B. Fernandez ◽  
R. E. Barr
Keyword(s):  
Neural Network ◽  
Hybrid System ◽  
Elbow Flexion ◽  
Learning System ◽  
Joint Torque ◽  
Neural Network Approach ◽  
Electromyographic Signal ◽  
Muscle Activations ◽  
Skeletal Model ◽  
Ballistic Movements

A hybrid modeling structure composed of a one degree of freedom computational musculo-skeletal model and a multilayer perceptron neural network was used to effectively map electromyography (EMG) from a human exercise trial to muscle activations in a physiologically feasible and accurate fashion. Several configurations of the complete hybrid system were used to map four muscle surface EMGs from a ballistic elbow flexion to normalized muscle activations, estimated individual muscle forces and torque about the joint. The net joint torque was used to train the neural portion of the hybrid system to minimize kinematic error. The model allowed the estimation of the nonobservable parameters: normalized muscle activations and forces which was used to penalize the learning system. With these parameters in the learning equation, our system produced muscle activations consistent with the classic triphasic response present in ballistic movements.

scholarly journals Development and Evaluation of a Musculoskeletal Model of the Elbow Joint Complex

1996 ◽  
Vol 118 (1) ◽  
pp. 32-40 ◽  
Author(s):  
R. V. Gonzalez ◽  
E. L. Hutchins ◽  
R. E. Barr ◽  
L. D. Abraham
Keyword(s):  
Elbow Joint ◽  
Muscle Force ◽  
Elbow Flexion ◽  
Musculoskeletal Model ◽  
Joint Torque ◽  
Time Varying ◽  
Moment Arm ◽  
Flexion Extension ◽  
The Relationship ◽  
Ballistic Movements

This paper describes the development and evaluation of a musculoskeletal model that represents human elbow flexion-extension and forearm pronation-supination. The length, velocity, and moment arm for each of the eight musculotendon actuators were based on skeletal anatomy and joint position. Musculotendon parameters were determined for each actuator and verified by comparing analytical moment-angle curves with experimental joint torque data. The parameters and skeletal geometry were also utilized in the musculoskeletal model for the analysis of ballistic (rapid-directed) elbow joint complex movements. The key objective was to develop a computational model, guided by parameterized optimal control, to investigate the relationship among patterns of muscle excitation, individual muscle forces, and to determine the effects of forearm and elbow position on the recruitment of individual muscles during a variety of ballistic movements. The model was partially verified using experimental kinematic, torque, and electromyographic data from volunteer subjects performing both isometric and ballistic elbow joint complex movements. This verification lends credibility to the time-varying muscle force predictions and the recruitment of muscles that contribute to both elbow flexion-extension and forearm pronation-supination.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Assessment of Self-Heating Susceptibility of Indian Coal Seams – A Neural Network Approach

2014 ◽  
Vol 59 (4) ◽  
pp. 1061-1076 ◽  
Author(s):  
D.C. Panigrahi ◽  
S.K. Ray
Keyword(s):  
Neural Network ◽  
Spontaneous Combustion ◽  
Potential Method ◽  
Oxidation Potential ◽  
Wet Oxidation ◽  
Experimental Conditions ◽  
Neural Network Approach ◽  
Indian Coal ◽  
Petrographic Analyses ◽  
Artificial Neural Network Ann

Abstract The paper addresses an electro-chemical method called wet oxidation potential technique for determining the susceptibility of coal to spontaneous combustion. Altogether 78 coal samples collected from thirteen different mining companies spreading over most of the Indian Coalfields have been used for this experimental investigation and 936 experiments have been carried out by varying different experimental conditions to standardize this method for wider application. Thus for a particular sample 12 experiments of wet oxidation potential method were carried out. The results of wet oxidation potential (WOP) method have been correlated with the intrinsic properties of coal by carrying out proximate, ultimate and petrographic analyses of the coal samples. Correlation studies have been carried out with Design Expert 7.0.0 software. Further, artificial neural network (ANN) analysis was performed to ensure best combination of experimental conditions to be used for obtaining optimum results in this method. All the above mentioned analysis clearly spelt out that the experimental conditions should be 0.2 N KMnO4 solution with 1 N KOH at 45°C to achieve optimum results for finding out the susceptibility of coal to spontaneous combustion. The results have been validated with Crossing Point Temperature (CPT) data which is widely used in Indian mining scenario.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach
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