scholarly journals Implementation of Preisach-Krasnoselskii Hysteresis Model with the Use of Artificial Neural Networks

2021 ◽  
Author(s):  
Zhao Wang
Keyword(s):  
Neural Networks ◽  
Magnetic Hysteresis ◽  
Feedforward Neural Networks ◽  
Hysteresis Model ◽  
Electromagnetic Devices ◽  
Hysteresis Modeling ◽  
Simulation Results ◽  
And Performance ◽  
Hysteresis Phenomena ◽  
Double Integrals

Accurate modeling of hysteresis is essential for both the design and performance evaluation of electromagnetic devices. This project proposes the use of feedforward meural networks to implement an accurate magnetic hysteresis model based on the mathematical difinition provided by the Preisach-Krasnoselskii (P-K) model. Feedforward neural networks are a linear association networks that relate the ouput patterns to input patterns. By introducing the multi-layer feedforward neural networks make the hysteresis modeling accurate without estimation of double integrals. Simulation results provide the detailed illustrations. The comparisons with the experiments show that the proposed approach is able to satisfactorily reproduce many features of obsereved hysteresis phenomena an in turn can be used for many applications of interest.

scholarly journals Implementation of Preisach-Krasnoselskii Hysteresis Model with the Use of Artificial Neural Networks

2021 ◽  
Author(s):  
Zhao Wang
Keyword(s):  
Neural Networks ◽  
Magnetic Hysteresis ◽  
Feedforward Neural Networks ◽  
Hysteresis Model ◽  
Electromagnetic Devices ◽  
Hysteresis Modeling ◽  
Simulation Results ◽  
And Performance ◽  
Hysteresis Phenomena ◽  
Double Integrals

Accurate modeling of hysteresis is essential for both the design and performance evaluation of electromagnetic devices. This project proposes the use of feedforward meural networks to implement an accurate magnetic hysteresis model based on the mathematical difinition provided by the Preisach-Krasnoselskii (P-K) model. Feedforward neural networks are a linear association networks that relate the ouput patterns to input patterns. By introducing the multi-layer feedforward neural networks make the hysteresis modeling accurate without estimation of double integrals. Simulation results provide the detailed illustrations. The comparisons with the experiments show that the proposed approach is able to satisfactorily reproduce many features of obsereved hysteresis phenomena an in turn can be used for many applications of interest.

Analysis of Quantization Effects on Higher Order Function and Multilayer Feedforward Neural Networks

2010 ◽  
pp. 187-222 ◽  
Author(s):  
Minghu Jiang ◽  
Georges Gielen ◽  
Lin Wang
Keyword(s):  
Neural Networks ◽  
Fixed Point ◽  
Statistical Models ◽  
Feedforward Neural Networks ◽  
Performance Degradation ◽  
Higher Order ◽  
Floating Point ◽  
Order Function ◽  
Network Layers ◽  
And Performance

In this chpater we investigate the combined effects of quantization and clipping on Higher Order function neural networks (HOFNN) and multilayer feedforward neural networks (MLFNN). Statistical models are used to analyze the effects of quantization in a digital implementation. We analyze the performance degradation caused as a function of the number of fixed-point and floating-point quantization bits under the assumption of different probability distributions for the quantized variables, and then compare the training performance between situations with and without weight clipping, and derive in detail the effect of the quantization error on forward and backward propagation. No matter what distribution the initial weights comply with, the weights distribution will approximate a normal distribution for the training of floating-point or high-precision fixed-point quantization. Only when the number of quantization bits is very low, the weights distribution may cluster to ±1 for the training with fixed-point quantization. We establish and analyze the relationships for a true nonlinear neuron between inputs and outputs bit resolution, training and quantization methods, the number of network layers, network order and performance degradation, all based on statistical models, and for on-chip and off-chip training. Our experimental simulation results verify the presented theoretical analysis.

Hysteresis modeling and feedforward compensation of a flexible structure actuated by macro fiber composites using bias bipolar Prandtl-Ishlinskii model

2021 ◽  
pp. 1045389X2199588
Author(s):  
Jinqiu Xu ◽  
Junqiang Lou ◽  
Yiling Yang ◽  
Tehuan Chen ◽  
Hairong Chen ◽  
...  
Keyword(s):  
Dead Zone ◽  
Fiber Composites ◽  
Piezoelectric Composite ◽  
Flexible Structure ◽  
Hysteresis Model ◽  
Triangular Wave ◽  
Hysteresis Nonlinearity ◽  
Hysteresis Modeling ◽  
And Performance ◽  
High Deformability

As a novel fiber-based piezoelectric composite material, macro fiber composites (MFC) affords notable advantages of good flexibility, high deformability, and large actuation ability. However, the intrinsic hysteresis behavior of the MFC decreases the positioning precision and performance of the flexible structure actuated by MFC actuators. A bias bipolar Prandtl-Ishlinskii (BBPI) model is presented to describe the bias bipolar hysteresis nonlinearity of a MFC-actuated flexible cantilever. The BBPI hysteresis model is composed of two parts: a superposition of the weighted play operators of the classical Prandtl-Ishlinskii (PI) model is employed to characterize the symmetric hysteresis. And a superposition of the weighted dead-zone operators is cascaded to deal with the bias bipolar behavior. Experimental identification results demonstrate that the presented BBPI model exhibits better modeling performance than the classical PI model. A feedforward compensation strategy based on the inverse BBPI hysteresis model is proposed. Experiments on trajectories tracking subject to a triangular wave, sinusoidal wave, and triangular wave with random amplitudes are carried out. Experimental results demonstrate the feasibility and effectiveness of the proposed BBPI model and the inverse feedforward compensator.

scholarly journals Research and Application of Improved AGP Algorithm for Structural Optimization Based on Feedforward Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Ruliang Wang ◽  
Huanlong Sun ◽  
Benbo Zha ◽  
Lei Wang
Keyword(s):  
Neural Networks ◽  
Structural Optimization ◽  
Network Performance ◽  
Feedforward Neural Networks ◽  
Improved Method ◽  
Pruning Algorithm ◽  
Network Pruning ◽  
Internal Relation ◽  
Traffic Capacity ◽  
Simulation Results

The adaptive growing and pruning algorithm (AGP) has been improved, and the network pruning is based on the sigmoidal activation value of the node and all the weights of its outgoing connections. The nodes are pruned directly, but those nodes that have internal relation are not removed. The network growing is based on the idea of variance. We directly copy those nodes with high correlation. An improved AGP algorithm (IAGP) is proposed. And it improves the network performance and efficiency. The simulation results show that, compared with the AGP algorithm, the improved method (IAGP) can quickly and accurately predict traffic capacity.

Prediction of molecular-dynamics simulation results using feedforward neural networks: Reaction of a C2 dimer with an activated diamond (100) surface

2005 ◽  
Vol 123 (22) ◽  
pp. 224711 ◽  
Author(s):  
Paras M. Agrawal ◽  
Abdul N. A. Samadh ◽  
Lionel M. Raff ◽  
Martin T. Hagan ◽  
Satish T. Bukkapatnam ◽  
...  
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Feedforward Neural Networks ◽  
Dynamics Simulation ◽  
Simulation Results

scholarly journals Hysteresis Modeling and Compensation of Fast Steering Mirrors with Hysteresis Operator Based Back Propagation Neural Networks

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 732
Author(s):  
Kairui Cao ◽  
Guanglu Hao ◽  
Qingfeng Liu ◽  
Liying Tan ◽  
Jing Ma
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Hysteresis Model ◽  
Cooperative Movement ◽  
Hysteresis Operator ◽  
The Neural Network ◽  
Hysteresis Nonlinearity ◽  
Hysteresis Modeling ◽  
Hysteresis Characteristics ◽  
Inverse Hysteresis

Fast steering mirrors (FSMs), driven by piezoelectric ceramics, are usually used as actuators for high-precision beam control. A FSM generally contains four ceramics that are distributed in a crisscross pattern. The cooperative movement of the two ceramics along one radial direction generates the deflection of the FSM in the same orientation. Unlike the hysteresis nonlinearity of a single piezoelectric ceramic, which is symmetric or asymmetric, the FSM exhibits complex hysteresis characteristics. In this paper, a systematic way of modeling the hysteresis nonlinearity of FSMs is proposed using a Madelung’s rules based symmetric hysteresis operator with a cascaded neural network. The hysteresis operator provides a basic hysteresis motion for the FSM. The neural network modifies the basic hysteresis motion to accurately describe the hysteresis nonlinearity of FSMs. The wiping-out and congruency properties of the proposed method are also analyzed. Moreover, the inverse hysteresis model is constructed to reduce the hysteresis nonlinearity of FSMs. The effectiveness of the presented model is validated by experimental results.

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