scholarly journals Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System

2021 ◽  
Vol 19 (2) ◽  
pp. 190-199
Author(s):  
Rati Wongsathan ◽  
Pornchai Supnithi
Keyword(s):  
Data Storage ◽  
Magnetic Recording ◽  
Back Propagation ◽  
Mean Square ◽  
Least Mean Square ◽  
Back Propagation Algorithm ◽  
Error Performance ◽  
Detector Performance ◽  
Neuro Fuzzy ◽  
Nonlinear Distortions

Nonlinear distortions caused by partial erasure and nonlinear transition shifts interacting with inter-symbol interference, are a major hindrance to data storage systems, since they degrade detector performance. This work aims to design and optimize the neuro-fuzzy equalizer (NFE) using the multi-objective genetic algorithm (MOGA) to detect nonlinear high-density magnetic recording (MR) channels. Through the GA-assisted back-propagation algorithm and least mean square optimization, the complexity in terms of decision rules is reduced by 25% and significantly provides 65% lower signal processing computation. When applied to the perpendicular (MR) system, the proposed NFE outperforms existing equalizers such as the neural network-based equalizer, fuzzy logic equalizer, and conventional NFE for the Volterra and jitter media noise channels using 1–3 dB and 1.5–3.5 dB signal-to-noise ratio gains at the bit-error-rate of 10-4, respectively. Furthermore, compared to the other models, the NFE provides a more effective output mean square error performance for retrieving the original bit data.

scholarly journals Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System

2010 ◽  
Vol 42 (1) ◽  
pp. 159-167
Author(s):  
Masumeh Sabet ◽  
Mehdi Naseri ◽  
Hosein Sabet
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Linear Process ◽  
Back Propagation Algorithm ◽  
Littoral Drift ◽  
Inference System ◽  
The Past ◽  
Neuro Fuzzy ◽  
Sand Drift

Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past, an accurate and reliable estimation of the rate of sand drift has still remained a problem. It is a non-linear process and can be described by chaotic time-series. The current study addresses this issue through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is about taking an initial fuzzy inference system (FIS) and tuning it with a back propagation algorithm based on the collection of input-output data. ANFIS was developed to predict the sand drift from a variety of causative variables. The structure and algorithm of ANFIS for predicting the rate of sand drift is described. The Adaptive Neuro-Fuzzy Inference System was validated by confirming its consistency with a database of specified physical process.

scholarly journals Development Multiple Neuro-Fuzzy System Using Back-propagation Algorithm

2013 ◽  
Vol 6 (2) ◽  
pp. 794-804
Author(s):  
Dr. Imad S. Alshawi ◽  
Haider Khalaf Allamy ◽  
Dr. Rafiqul Zaman Khan
Keyword(s):  
Fuzzy System ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Back Propagation Algorithm ◽  
New Approach ◽  
Training Time ◽  
Propagation Algorithm ◽  
Neuro Fuzzy ◽  
Highly Nonlinear ◽  
Input Variables

When fuzzy systems are highly nonlinear or include a large number of input variables, the number of fuzzy rules constituting the underlying model is usually large. Dealing with a large-size fuzzy model may face many practical problems in terms of training time, ease of updating, generalizing ability and interpretability. Multiple Fuzzy System (MFS) is one of effective methods to reduce the number of rules, increase the speed to obtain good results. This paper is therefore proposes another approach call Multiple Neuro-Fuzzy System (MNFS) which can further enhance the performance of the MFS approach. The new approach is used Back-propagation algorithm in the learning process. The performance of the proposed approach evaluates and compares with MFS by three experiments on nonlinear functions. Simulation results demonstrate the effectiveness of the new approach than MFS with regards to enhancement of the accuracy of the results.  

scholarly journals Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy

2018 ◽  
pp. 1-9
Author(s):  
Ibrahim Goni ◽  
Christopher U. Ngene ◽  
Manga I. ◽  
Auwal Nata’ala ◽  
Sunday J. Calvin
Keyword(s):  
Fuzzy Inference ◽  
Intelligent System ◽  
Research Work ◽  
Back Propagation ◽  
Night Sweat ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
History Of ◽  
Loss Of Appetite

Tuberculosis is a contiguous disease that is causing death both in developed and developing countries. The main aim of this research work was to a developed an intelligent system for diagnosing Tuberculosis using adaptive neuro-fuzzy methodology. Eleven symptoms of tuberculosis which are persistent cough for more than two weeks, cough with blood, weight loss, tiredness, chest pain, fever, difficulty in breathing, loss of appetite, lymph node enlargement, history of TB contact and night Sweat are assigned with weights which are categorize best on severity level as mild, moderate, severe and very severe, yes and no which serve as inputs to the adaptive neuro-fuzzy inference system (ANFIS). MATLAB 7.0 is used to implement this experiment, Trapezoidal Membership function was used, back propagation algorithm was used for training and testing, the error obtain is 0.41777 at epoch 2 which shows that the training performance is exactly 99.58223 and testing performance of the system are 99.58197 at epoch 2.   

Hybrid Intelligent Systems for Medical Diagnosis

2010 ◽  
pp. 187-202 ◽  
Author(s):  
Rahul Kala ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Medical Diagnosis ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Medical Engineering ◽  
Back Propagation Algorithm ◽  
Hybrid Intelligent Systems ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Inference Systems

In this chapter authors describe the application of various soft computing techniques in the field of medical diagnosis. They also explained new approaches being applied to the field of Bio-Medical Engineering as well as many new models being proposed, like Hybrid Systems and standard Back Propagation Algorithm for this purpose. These are Adaptive Neuro Fuzzy Inference Systems, Ensembles and Evolutionary Artificial Neural Networks.

scholarly journals Accurate Parameter Estimation for Unbalanced Three-Phase System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan Chen ◽  
Hing Cheung So
Keyword(s):  
Parameter Estimation ◽  
Frequency Estimation ◽  
Nonlinear Least Squares ◽  
Phase System ◽  
Mean Square ◽  
Least Mean Square ◽  
Error Performance ◽  
Control Console ◽  
Three Phase ◽  
And Control

Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via theαβ-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newton-Raphson scheme, whose global convergence is studied in this paper. Computer simulations show that the mean square error performance of NLS method can attain the Cramér-Rao lower bound. Moreover, our proposal provides more accurate frequency estimation when compared with the complex least mean square (CLMS) and augmented CLMS.

scholarly journals Comparison of mathematical models and artificial neural networks for prediction of drying kinetics of mushroom in microwave vacuum dryer

2012 ◽  
Vol 18 (2) ◽  
pp. 283-293 ◽  
Author(s):  
A. Ghaderi ◽  
S. Abbasi ◽  
A. Motevali ◽  
S. Minaei
Keyword(s):  
Experimental Data ◽  
Mean Square Error ◽  
Back Propagation ◽  
Percentage Error ◽  
Absolute Pressure ◽  
Mean Square ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Drying Time ◽  
Thin Layer Drying

Drying characteristics of button mushroom slices were determined using microwave vacuum drier at various powers (130, 260, 380, 450 W) and absolute pressures (200, 400, 600, 800 mbar). To select a suitable mathematical model, 6 thin-layer drying models were fitted to the experimental data. The fitting rates of models were assessed based on three parameters; highest R2, lowest chi square () and root mean square error (RMSE). In addition, using the experimental data, an ANN trained by standard back-propagation algorithm, was developed in order to predict moisture ratio (MR) and drying rate (DR) values based on the three input variables (drying time, absolute pressure, microwave power). Different activation functions and several rules were used to assess percentage error between the desired and the predicted values. According to our findings, Midilli et al. model showed a reasonable fitting with experimental data. While, the ANN model showed its high capability to predict the MR and DR quite well with determination coefficients (R2) of 0.9991, 0.9995 and 0.9996 for training, validation and testing, respectively. Furthermore, their predictions Mean Square Error were 0.00086, 0.00042 and 0.00052, respectively.

scholarly journals Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7732
Author(s):  
Azam Khalili ◽  
Vahid Vahidpour ◽  
Amir Rastegarnia ◽  
Ali Farzamnia ◽  
Kenneth Teo Tze Kin ◽  
...  
Keyword(s):  
Convergence Rate ◽  
Data Exchange ◽  
Learning Curves ◽  
Coordinate Descent ◽  
Mean Square ◽  
Least Mean Square ◽  
Local Estimate ◽  
Error Performance ◽  
Multi Agent ◽  
Agent Networks

The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. However, in some practical situations, perfect data exchange may not be possible among the nodes. In this paper, we develop a new version of ILMS algorithm, wherein in its adaptation step, only a random subset of the coordinates of update vector is available. We draw a comparison between the proposed coordinate-descent incremental LMS (CD-ILMS) algorithm and the ILMS algorithm in terms of convergence rate and computational complexity. Employing the energy conservation relation approach, we derive closed-form expressions to describe the learning curves in terms of excess mean-square-error (EMSE) and mean-square deviation (MSD). We show that, the CD-ILMS algorithm has the same steady-state error performance compared with the ILMS algorithm. However, the CD-ILMS algorithm has a faster convergence rate. Numerical examples are given to verify the efficiency of the CD-ILMS algorithm and the accuracy of theoretical analysis.

scholarly journals Track servo patterns spacing optimization using least mean square estimation algorithm for holographic data storage

2013 ◽  
Vol 9 (1) ◽  
pp. 5-9
Author(s):  
Sung-Yong Lim ◽  
JongJin Lee ◽  
Jae-Seong Lee ◽  
Wooyoung Jeong ◽  
Hyunseok Yang ◽  
...  
Keyword(s):  
Data Storage ◽  
Estimation Algorithm ◽  
Holographic Data Storage ◽  
Mean Square ◽  
Least Mean Square

Research of the Neural Network by Back Propagation Algorithm

2012 ◽  
Vol 463-464 ◽  
pp. 1151-1154 ◽  
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Liviu Ciupitu
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Least Mean Square ◽  
Training Algorithm ◽  
Back Propagation Algorithm ◽  
Virtual Instrumentation ◽  
Delta Rule ◽  
The Neural Network ◽  
The Mathematical Model

In the control of the position of the robots systems one of the more important is to assure the minimum errors between the output and the target. All advanced researches in the word propose to use the neural network (NN) and the learning algorithm like Widrow and Hoff, or Levenberg-Marquard by using the least mean square (LMS) of errors and Delta rule, or back propagation training algorithm. Present paper is showing the mathematical model and numerical simulation of some important neurons types used in many applications that require extreme precision and neural network. All assisted researches were made with the owner LabVIEW virtual instrumentation. The research results and virtual LabVIEW instrumentation can be used in many other mechatronics applications.

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