The Use of Vehicle Dynamic Response to Estimate Road Profile Input in Time Domain

2014 ◽  
Author(s):  
Yechen Qin ◽  
Reza Langari ◽  
Liang Gu
Keyword(s):  
Time Domain ◽  
System Analysis ◽  
Least Square ◽  
System Response ◽  
Group Method ◽  
Vehicle Dynamic ◽  
Recursive Least Square ◽  
Inference System ◽  
Vehicle System ◽  
Road Profile

A new method for road profile estimation in time domain with the application of vehicle system response was presented in this paper, and the problem was transformed as a system identification issue for an inverse nonlinear quarter vehicle model. Firstly, the inverse vehicle dynamic model was trained with specifically chosen white noise signal, and then eight different types of membership functions (MF) for Adaptive Neuro Fuzzy Inference System (ANFIS) were compared. Finally, the comparison of three different methods: ANFIS, Recursive Least Square (RLS) and Group Method of Data Handling (GMDH) were researched with different vehicle speeds and different road levels in the simulation part. The results showed that ANFIS is better in comparison with RLS and GMDH and this method can be further applied for vehicle system analysis.

Post Optimal System Analysis Using Aggregated Design Impact Matrix

2000 ◽  
Author(s):  
Petter Krus
Keyword(s):  
Gradient Method ◽  
System Analysis ◽  
System Development ◽  
Least Square ◽  
Optimal System ◽  
Relative Importance ◽  
Recursive Least Square ◽  
Established Procedure ◽  
System Characteristics

Abstract In this paper the concept of the Aggregated Design Impact Matrix, ADIM, is introduced. This is a tool to calculate and present the relative importance of whole components and subsystems (instead of individual parameters) on different system characteristics. Optimisation is a well-established procedure for system development. Here a non-gradient method is used. Although the sensitivities are not calculated explicitly they can be estimated from the sequence of parameter sets evaluated during the optimisation. The technique used here is recursive least square.

Nonlinear Dynamic Analysis of a Rotor System With Aerodynamic Journal Bearings

2007 ◽  
Author(s):  
T. N. Shiau ◽  
W. C. Hsu ◽  
B. W. Deng
Keyword(s):  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Analysis ◽  
Journal Bearings ◽  
Rotor System ◽  
Least Square ◽  
Filter Method ◽  
System Response ◽  
Recursive Least Square ◽  
Bearing Force ◽  
Rotor Mass

This paper investigates nonlinear dynamic characteristics of a rotor system with aerodynamic journal bearings. The Finite Difference Method (FDM) is employed to solve the Reynolds equation, which is used to determine the nonlinear compressible gas force of the aerodynamic bearing. By applying the gas bearing force to system equations of motion, the system response can be determined by the numerical integration method. Results show that the aerodynamic bearing will provide higher loading capacity to support the rotor when the eccentricity ratio is increased. The aerodynamic bearing force increases when the rotor is speeding up or the squeeze frequency is raised. The rotor trajectory presents aperiodic behavior, and it becomes significant as the rotor mass increases. When the squeeze frequency decreases or the rotor mass increases, the radius of the rotor trajectory will increase. Recursive Least Square Method and Kalman Filter Method are used to identify the aerodynamic bearing parameters from the system response. The parameters include the damping and stiffness coefficients of the aerodynamic bearing. According to the results of identification, both identified parameters by these two methods are in good accordance. The results show that the aerodynamic bearing force can be precisely identified and the system response can be quickly solved by the identified system with less computer time. But the identified system lost its accuracy as the rotor speed or the squeeze frequency increase because these will enhance the nonlinearity of the aerodynamic bearing force.

scholarly journals Dynamic evolving neural fuzzy inference system equalization scheme in mode division multiplexer for optical fiber transmission

2019 ◽  
Vol 8 (1) ◽  
pp. 127-135 ◽  
Author(s):  
Awab Noori ◽  
Angela Amphawan ◽  
Alaan Ghazi ◽  
S. A. Aljunid Ghazi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Transmission Rate ◽  
Least Square ◽  
Optical Systems ◽  
Recursive Least Square ◽  
Inference System ◽  
Linear Equalization ◽  
Online Implementation ◽  
Neural Fuzzy

The performance of optical mode division multiplexer (MDM) is affected by inter-symbol interference (ISI), which arises from higher-order mode coupling and modal dispersion in multimode fiber (MMF). Existing equalization algorithms in MDM can mitigate linear channel impairments, but cannot tackle nonlinear channel impairments accurately. Therefore, mitigating the noise in the received signal of MDM in the presence of ISI to recover the transmitted signal is important issue. This paper aims at controlling the broadening of the signal from MDM and minimizing the undesirable noise among channels. A dynamic evolving neural fuzzy inference system (DENFIS) equalization scheme has been used to achieve this objective. Results illustrate that nonlinear DENFIS equalization scheme can improve the received distorted signal from an MDM with better accuracy than previous linear equalization schemes such as recursive‐least‐square (RLS) algorithm. Desirably, this effect allows faster data transmission rate in MDM. Additionally, the successful offline implementation of DENFIS equalization in MDM encourages future online implementation of DENFIS equalization in embedded optical systems.

INTELLIGENT FUZZY WEIGHTED INPUT ESTIMATION METHOD FOR THE DYNAMIC FORCE INPUTS OF A CANTILEVER BEAM MOUNTED WITH CONCENTRATED MASSES

2012 ◽  
Vol 04 (01) ◽  
pp. 1250005 ◽  
Author(s):  
MING-HUI LEE
Keyword(s):  
Kalman Filter ◽  
Cantilever Beam ◽  
Dynamic Performance ◽  
Estimation Method ◽  
Weighting Factor ◽  
Least Square ◽  
Dynamic Force ◽  
Recursive Least Square ◽  
Inference System ◽  
Input Estimation

This study proposes an intelligent fuzzy weighted input estimation method for the force inputs of a cantilever beam structural system. The finite element scheme is employed to discretize the problem in space, allowing multi-dimensional problems of various geometries to be treated. The Kalman filter (KF) and the recursive least square estimator (RLSE) are two main portions of this method. In this method, the efficient estimator is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. By directly synthesizing the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable tradeoff between the tracking capability and the flexibility against noises. The input forces of structural sytem can be estimated by this method to promote the analysis reliability of the dynamic performance. The simulation results are compared by alternating between the constant and adaptive weighting factors. The results demonstrate that the application of the presented method is successful in coping with the dynamic system of cantilever beam.

A HYBRID METHOD OF MODIFIED CAT SWARM OPTIMIZATION AND GRADIENT DESCENT ALGORITHM FOR TRAINING ANFIS

2013 ◽  
Vol 12 (02) ◽  
pp. 1350007 ◽  
Author(s):  
MEYSAM OROUSKHANI ◽  
MOHAMMAD MANSOURI ◽  
YASIN OROUSKHANI ◽  
MOHAMMAD TESHNEHLAB
Keyword(s):  
Gradient Descent ◽  
Fuzzy Inference ◽  
Least Square ◽  
Training Methods ◽  
Recursive Least Square ◽  
Swarm Optimization ◽  
Inference System ◽  
Descent Algorithm ◽  
Cat Swarm Optimization ◽  
Gradient Descent Algorithm

This paper introduces a novel approach for tuning the parameters of the adaptive network-based fuzzy inference system (ANFIS). In the commonly used training methods, the antecedent and consequent parameters of ANFIS are trained by gradient-based algorithms and recursive least square method, respectively. In this study, a new swarm-based meta-heuristic optimization algorithm, so-called "Cat Swarm Optimization", is used in order to train the antecedent part parameters and gradient descent algorithm is applied for training the consequent part parameters. Experimental results for prediction of Mackey–Glass model and identification of two nonlinear dynamic systems reveal that the performance of proposed algorithm is much better and it shows quite satisfactory results.

A Time-Domain Method for Identifying Modal Parameters

1986 ◽  
Vol 53 (1) ◽  
pp. 28-32 ◽  
Author(s):  
Zhen-ni Wang ◽  
Tong Fang
Keyword(s):  
Time Domain ◽  
Arma Model ◽  
Time Domain Method ◽  
Least Square ◽  
Ar Model ◽  
Modal Parameters ◽  
System Response ◽  
Domain Method ◽  
Linear Least Square ◽  
Digital Simulations

A time-domain method for identifying the modal parameters of a vibration system is presented. It is shown that system eigenvectors can be effectively estimated through the multivariate AR model representation of the system response to white noise excitation. In contrast to the usual ARMA model approach, in this method only a linear least square algorithm is required, so that a great amount of calculation is saved. Results of digital simulations support the identification method.

scholarly journals Multiple Function Approximation — A New Approach Using Asymmetric Complex Fuzzy Inference System

2019 ◽  
Vol 06 (04) ◽  
pp. 407-422
Author(s):  
Chia-Hao Tu ◽  
Chunshien Li
Keyword(s):  
Fuzzy Inference System ◽  
Function Approximation ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Least Square ◽  
Asymmetric Structure ◽  
Neural Net ◽  
Recursive Least Square ◽  
Inference System ◽  
Proposed Model

This paper proposes an asymmetric complex fuzzy inference system (ACFIS) that improves a conventional fuzzy inference system (FIS) in two ways. First, the proposed model uses the novel neural-net-like aim–object parts, making the model flexible, in terms of model size of parameters and terse asymmetric structure. Second, the enhanced complex fuzzy sets (ECFSs) are used to expand membership degree from a single real-valued state to complex-valued vector state. Hence, the ACFIS can have the ability to predict multiple targets simultaneously. In addition, a hybrid learning algorithm, combining the particle swarm optimization (PSO) and the recursive least-square estimator (RLSE), is utilized to optimize the proposed model. To test the proposed approach, we did experimentation on four-function approximation using one single model only with 10 repeated trails. Based on the experimental results, the ACFIS has shown excellent performance.

Transmission Line Protective Relay Based on Recursive Least-Square Filters and Weights Analysis

2018 ◽  
Vol 11 (5) ◽  
pp. 277 ◽  
Author(s):  
David Felipe Celeita Rodriguez ◽  
Gustavo Ramos ◽  
A. P. Sakis Meliopoulos
Keyword(s):  
Transmission Line ◽  
Least Square ◽  
Recursive Least Square ◽  
Protective Relay
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