scholarly journals Identification of Mimo dynamic system using inverse Mimo Neural Narx model

2013 ◽  
Vol 16 (2) ◽  
pp. 13-25
Author(s):  
Anh Pham Huy Ho ◽  
Nam Thanh Nguyen
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Artificial Muscle ◽  
Training Data ◽  
The Novel ◽  
Robot Arm ◽  
Dynamic Inverse ◽  
Intelligent Model ◽  
First Time ◽  
Narx Model

This paper investigates the application of proposed neural MIMO NARX model to a nonlinear 2-axes pneumatic artificial muscle (PAM) robot arm as to improve its performance in modeling and identification. The contact force variations and nonlinear coupling effects of both joints of the 2-axes PAM robot arm are modeled thoroughly through the novel dynamic inverse neural MIMO NARX model exploiting experimental input-output training data. For the first time, the dynamic neural inverse MIMO NARX Model of the 2-axes PAM robot arm has been investigated. The results show that this proposed dynamic intelligent model trained by Back Propagation learning algorithm yields both of good performance and accuracy. The novel dynamic neural MIMO NARX model proves efficient for modeling and identification not only the 2-axes PAM robot arm but also other nonlinear dynamic systems.

A NEW APPROACH OF THE 2-AXES PAM ROBOT ARM IDENTIFICATION USING NEURAL MIMO NARX MODEL

2013 ◽  
Vol 22 (01) ◽  
pp. 1250039
Author(s):  
HO PHAM HUY ANH ◽  
KYOUNG KWAN AHN
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Training Data ◽  
The Novel ◽  
Robot Arm ◽  
New Approach ◽  
Identification Process ◽  
Highly Nonlinear ◽  
Model Scheme ◽  
Narx Model

In this paper, a novel MIMO Neural NARX model is used for simultaneously modeling and identifying both joints of the 2-axes PAM robot arm's inverse and forward dynamic model. The highly nonlinear cross effect of both links of the 2-axes PAM robot arm are thoroughly modeled through an Inverse and Forward Neural MIMO NARX Model-based identification process using experimental input-output training data. Consequently the proposed Inverse and Forward Neural MIMO NARX model scheme of the nonlinear 2-axes PAM robot arm has been investigated. The results show that the novel Inverse and Forward Neural MIMO NARX Model trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.

scholarly journals Implementation supervisory controller for hybrid wind microgrid system using adaptive neural MIMO model

2015 ◽  
Vol 18 (3) ◽  
pp. 65-75
Author(s):  
Anh Pham Huy Ho ◽  
Son Ngoc Nguyen ◽  
Huan Thien Tran
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Neural Model ◽  
Bp Algorithm ◽  
Training Data ◽  
The Novel ◽  
Identification Process ◽  
Supervisory Controller ◽  
Nonlinear Features ◽  
Mimo Model

This paper investigates a novel forward adaptive neural model which is applied for modeling and implementing the supervisory controller of the hybrid wind microgrid system. The nonlinear features of the hybrid wind microgrid system are thoroughly modeled based on the adaptive identification process using experimental input-output training data. This paper proposes the novel use of a back propagation (BP) algorithm to generate the adaptive neural-based supervisory controller for the hybrid wind microgrid system. The simulation results show that the proposed adaptive neuralbased supervisory controller trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.

scholarly journals A LEARNING METHOD TO DETERMINE HOW TO APPROACH AN UNKNOWN OBJECT TO BE GRASPED

2011 ◽  
Vol 08 (03) ◽  
pp. 579-606 ◽  
Author(s):  
BENJAMIN D. BALAGUER ◽  
STEFANO CARPIN
Keyword(s):  
Learning Algorithm ◽  
Training Session ◽  
Training Data ◽  
Support Vector ◽  
The Novel ◽  
Learning Method ◽  
End Effector ◽  
Novel Object ◽  
Time Performance ◽  
Feature Based

We present a learning algorithm to determine the appropriate approaching pose to grasp a novel object. Our method focuses on the computation of valid end-effector orientations in order to make contact with the object at a given point. The system achieves this goal by generalizing from positive examples provided by a human operator during an offline training session. The technique is feature-based since it extracts salient attributes of the object to be grasped rather than relying on the availability of models or trying to build one. To compute the desired orientation, the robot performs three steps at run time. Using a multi-class Support Vector Machine (SVM), it first classifies the novel object into one of the object classes defined during training. Next, it determines its orientation, and, finally, based on the classification and orientation, it extracts the most similar example from the training data and uses it to grasp the object. The method has been implemented on a full-scale humanoid robotic torso equipped with multi-fingered hands and extensive results corroborate both its effectiveness and real-time performance.

Equipment Support Training Level Evaluation Method based on ANFIS

2013 ◽  
Vol 706-708 ◽  
pp. 1950-1953
Author(s):  
Wu Kui Zhao ◽  
Cheng Zhang ◽  
Yi Bo Wang
Keyword(s):  
Evaluation Method ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Evaluation Model ◽  
Back Propagation ◽  
Training Data ◽  
Training Level ◽  
Inference System ◽  
Proposed Model ◽  
Training Efficiency

The evaluation of equipment support training is an effective way to improve training efficiency. The main influencing factors of equipment support training are analyzed. Adaptive neural fuzzy inference system (ANFIS) model structure is established and the hybrid-learning algorithm to solve the established model by applying back-propagation and least mean squares procedure is investigated. Then the evaluation model of equipment support training level based on ANFIS is constructed. The training level consistent with the actual training level is achieved by training the proposed model using training data samples, which verifies the correctness and effectiveness of the proposed method. Simulation comparing analysis using the proposed method and BP neutral network is conducted respectively. The superiority of the proposed method is verified by simulation results, which provides an effective method for equipment support training evaluation.

Oil Palm Yield Forecasting Based on Weather Variables Using Artificial Neural Network

2016 ◽  
Vol 3 (3) ◽  
pp. 626
Author(s):  
Nadia Dwi Kartika ◽  
I Wayan Astika ◽  
Edi Santosa
Keyword(s):  
Neural Network ◽  
Oil Palm ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Secondary Data ◽  
Optimization Procedure ◽  
Training Data ◽  
Weather Variables ◽  
Feed Forward Neural Network ◽  
Yield Forecasting

Forecasting of oil palm yield has become a main factor in the management of oil palm industries for proper planning and decision making in order to avoid monthly high cost in harvesting. Predicting future value of oil palm yield with minimum error becomes an important issue recently. A lot of factors determine the productivity of oil palm and weather variables play an important role that affect plant growth and development that may reduce yield significantly. This research used secondary data of yield and weather variables available in company administration. It proposed feed forward neural network with back propagation learning algorithm to build a monthly yield forecasting model. The optimization procedure of ANN architecture obtained the best using 60 neurons in input layer, five hidden layers and one neuron in the output layer. Training data were from January 2005 to June 2008 while testing data were from July 2008 to December 2009. ANN architecture using five hidden layers gave the best accuracy with MAE 0.5346 and MSE 0.4707 while the lowest accuracy occurred by using two hidden layers with MAE 1.5843and MSE 4.087.

scholarly journals Dynamic model identification of IPMC actuator using fuzzy NARX model optimized by MPSO

2014 ◽  
Vol 17 (1) ◽  
pp. 62-80
Author(s):  
Anh Pham Huy Ho ◽  
Nam Thanh Nguyen
Keyword(s):  
Dynamic Model ◽  
Model Identification ◽  
Training Data ◽  
The Novel ◽  
Inverse Dynamic ◽  
Modified Particle Swarm Optimization ◽  
Highly Nonlinear ◽  
Actuator System ◽  
Force Variation ◽  
Narx Model

In this paper, a novel inverse dynamic fuzzy NARX model is used for modeling and identifying the IPMC-based actuator’s inverse dynamic model. The contact force variation and highly nonlinear cross effect of the IPMC-based actuator are thoroughly modeled based on the inverse fuzzy NARX model-based identification process using experiment input-output training data. This paper proposes the novel use of a modified particle swarm optimization (MPSO) to generate the inverse fuzzy NARX (IFN) model for a highly nonlinear IPMC actuator system. The results show that the novel inverse dynamic fuzzy NARX model trained by MPSO algorithm yields outstanding performance and perfect accuracy.

A neurofuzzy controller for active front steering system of vehicle under road friction uncertainties

2018 ◽  
Vol 41 (4) ◽  
pp. 1057-1067 ◽  
Author(s):  
B Aalizadeh
Keyword(s):  
Pid Controller ◽  
Fuzzy Controller ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Steering System ◽  
Training Data ◽  
Inference System ◽  
Vehicle Handling ◽  
Active Front Steering ◽  
Road Friction

In this study, a neurofuzzy controller is proposed to improve vehicle handling in different road friction coefficients. This controller adapts itself to neutralize the effects of unpredictable changes in road friction coefficient on vehicle handling. This adaptive neurofuzzy controller can improve vehicle handling, manoeuvrability and path tracking. First a proportional-integral-derivative (PID) controller is proposed and tuned by using PSO (particle swarm optimization). Then, this tuned PID controller is applied to the vehicle system and training data is gathered. The next step is to train a fuzzy controller by importing thistraining data tothe ANFIS (adaptive neurofuzzy inference system) toolbox of MATLAB software. Then the trained fuzzy controller is applied to a vehicle that exploits AFS (active front steering system). This controller is able to adapt itself during manoeuvres, by using back propagation of error as a learning algorithm. Results show that neurofuzzy controller can improve handling of the vehicle in different road conditions, because neurofuzzy conteroller can adapt itself in unpredictable situations.

scholarly journals Sentiment Classification of News Text Data Using Intelligent Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Shitao Zhang
Keyword(s):  
Language Processing ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Sentiment Classification ◽  
Training Data ◽  
Text Data ◽  
Discriminative Performance ◽  
Traffic Jam ◽  
Intelligent Model

Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase “traffic jam” expresses negative sentiment in the sentence “I was stuck in a traffic jam on the elevated for 2 h.” But in the domain of transportation, the phrase “traffic jam” in the sentence “Bread and water are essential terms in traffic jams” is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.

OPTIMAL HIDDEN UNITS FOR TWO-LAYER NONLINEAR FEEDFORWARD NEURAL NETWORKS

1991 ◽  
Vol 05 (04) ◽  
pp. 545-561 ◽  
Author(s):  
TERENCE D. SANGER
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Global Optimum ◽  
Constructive Method ◽  
Robot Arm ◽  
Back Propagation Algorithm ◽  
Closed Form Solutions ◽  
Nonlinear Features ◽  
Hidden Layer

The output layer of a feedforward neural network approximates nonlinear functions as a linear combination of a fixed set of basis functions, or "features". These features are learned by the hidden-layer units, often by a supervised algorithm such as a back-propagation algorithm. This paper investigates features which are optimal for computing desired output functions from a given distribution of input data, and which must therefore be learned using a mixed supervised and unsupervised algorithm. A definition is proposed for optimal nonlinear features, and a constructive method, which has an iterative implementation, is derived for finding them. The learning algorithm always converges to a global optimum and the resulting network uses two layers to compute the hidden units. The general form of the features is derived for the case of continuous signal input, and this result is related to transmission of information through a bandlimited channel. The results of other algorithms can he compared to the optimal features, which in some cases have easily computed closed-form solutions. The application of this technique to the inverse kinematics problem for a simulated planar two-joint robot arm is demonstrated here.

STEPPER MOTOR SYSTEM IDENTIFICATION USING INVERSE DYNAMIC NEURAL MIMO NARX MODEL

2010 ◽  
Vol 13 (4) ◽  
pp. 34-44
Author(s):  
Anh Pham Huy Ho ◽  
Lam Huynh Phan
Keyword(s):  
Motor System ◽  
Learning Algorithm ◽  
Model Identification ◽  
Training Data ◽  
Stepper Motor ◽  
Inverse Dynamic ◽  
Highly Nonlinear ◽  
Nonlinear Features ◽  
Mimo Model ◽  
Narx Model

This paper introduces the novel inverse dynamic intelligent MIMO model which is applied for modeling and identifying the stepper motor dynamic model. Hence the highly nonlinear features of stepper motor system are modeled thoroughly based on the inverse neural NARX model identification process using experimental input-output training data. Consequently the proposed inverse neural NARX MIMO model scheme of the nonlinear stepper motor has been investigated. The results showed that the proposed inverse neural NARX MIMO model trained by the back propogation learning algorithm (BP) yields outstanding performance and perfect accuracy.

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