scholarly journals COMPARATIVE ANALYSIS OF APPROXIMATION METHODS USING REGRESSION RELATIONSHIPS AND NEURAL NETWORKS FOR LINEAR MODELS

2021 ◽  
Vol 2021 (1) ◽  
pp. 31-35
Author(s):  
Svetlana Senotova
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Linear Models ◽  
Approximation Methods

The paper examines comparative analysis of approximation methods using regression dependencies and neural networks for linear models.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

Global Approximation: Performance Comparison of Different Methods, With an Application to Road Vehicle System Engineering

1999 ◽  
Author(s):  
Massimiliano Gobbi ◽  
Giampiero Mastinu
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Linear Interpolation ◽  
Performance Comparison ◽  
Pareto Optimal ◽  
Approximation Methods ◽  
Global Approximation ◽  
Road Vehicle ◽  
Pareto Optimal Set ◽  
Design Variables

Abstract Optimisation of complex mechanical systems has often to be performed by resorting to global approximation. In usual global approximation practice, the original mathematical model is substituted by another mathematical model which gives approximately the same relationships between design variables and performance indexes. This is made to ensure much faster simulations which are of crucial importance to find optimal solutions. In this paper the performances of four global approximation methods (Neural Networks, Kriging, Quadratic Approximation, Linear Interpolation) are compared, with reference to an actual optimal design problem. The performances of a road vehicle suspension system are optimised by varying the system’s design variables. The Pareto-optimal set is derived symbolically. The performances of the different approximation methods taken into consideration are assessed by comparing the numerical- and the analytical-Pareto-optimal results. It is found that Neural Networks obtain the best accuracy.

scholarly journals Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks

2019 ◽  
Vol 11 (4) ◽  
pp. 1 ◽  
Author(s):  
Tobias de Taillez ◽  
Florian Denk ◽  
Bojana Mirkovic ◽  
Birger Kollmeier ◽  
Bernd T. Meyer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Models ◽  
Transfer Functions ◽  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Related Potentials ◽  
Linear And Nonlinear ◽  
Artificial Neural

Diferent linear models have been proposed to establish a link between an auditory stimulus and the neurophysiological response obtained through electroencephalography (EEG). We investigate if non-linear mappings can be modeled with deep neural networks trained on continuous speech envelopes and EEG data obtained in an auditory attention two-speaker scenario. An artificial neural network was trained to predict the EEG response related to the attended and unattended speech envelopes. After training, the properties of the DNN-based model are analyzed by measuring the transfer function between input envelopes and predicted EEG signals by using click-like stimuli and frequency sweeps as input patterns. Using sweep responses allows to separate the linear and nonlinear response components also with respect to attention. The responses from the model trained on normal speech resemble event-related potentials despite the fact that the DNN was not trained to reproduce such patterns. These responses are modulated by attention, since we obtain significantly lower amplitudes at latencies of 110 ms, 170 ms and 300 ms after stimulus presentation for unattended processing in contrast to the attended. The comparison of linear and nonlinear components indicates that the largest contribution arises from linear processing (75%), while the remaining 25% are attributed to nonlinear processes in the model. Further, a spectral analysis showed a stronger 5 Hz component in modeled EEG for attended in contrast to unattended predictions. The results indicate that the artificial neural network produces responses consistent with recent findings and presents a new approach for quantifying the model properties.

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