An Extended Kalman Filter Based Decision Feedback Fuzzy Adaptive Equalizer For Power Line Channel (original) (raw)
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An Extended Kalman Filter Based Fuzzy Adaptive Equalizer For Powerline Channel
Fuzzy logic is the principles of imprecise knowledge. Fuzzy adaptive equalizers are adaptive equalizers that apply the concepts of fuzzy logic. The main merit of applying fuzzy adaptive equalizers in powerline channel equalization is that linguistic information (fuzzy IF-THEN rules) and numerical information (input-output pairs) can be combined into the equalizers. The adaptive algorithms adjust the parameters of the membership functions which characterize the fuzzy concepts in the IF-THEN rules, by minimizing some criterion function. In this paper, we introduce a new type of fuzzy adaptive equalizer using extended Kalman filter (EKF) algorithm for powerline channel equalization. The performance for this type of fuzzy adaptive equalizer is compared with two other types of fuzzy adaptive equalizers using recursive least squares (RLS) and least mean squares (LMS) adaption algorithm. The simulation results show that extended Kalman filter based fuzzy adaptive equalizer has faster convergent speed compared to the other two fuzzy adaptive equalizers.The bit error rate of extended Kalman filter based fuzzy adaptive equalizer is close to that of the optimal equalizer.
A robust and effective fuzzy adaptive equalizer for powerline communication channels
Fuzzy adaptive equalizers (FAEs) are adaptive equalizers that apply the concepts of fuzzy logic. The main merit of applying FAEs in powerline channel equalization is that linguistic information (fuzzy IF–THEN rules) and numerical information (input–output pairs) can be combined into the equalizers. The adaptive algorithms adjust the parameters of the membership functions which characterize the fuzzy concepts in the IF–THEN rules, by minimizing some criterion function. In this paper, we propose a new FAE, using the extended Kalman filter (EKF) algorithm for powerline channel equalization. The simulation results show that the EKF-based FAE has lower steady state bit error rate (BER) and faster convergent speed compared to decision feedback recursive least-squares adaptive equalizer, recursive least squares (RLS) based FAE and least mean quares (LMS) based FAE. We also propose a robust improvement scheme for the new FAE. Simulation results show that the performance of the proposed robust FAE is improved in powerline channel equalization and outperforms all the equalizers considered above. The BER of the proposed scheme is very close to the optimum performance.
Fuzzy adaptive equalizer (FAE) is a knowledge based equalizer operating on linguistic variables. The advantages of using fuzzy logic adaptation scheme with respect to more traditional adaptation schemes in powerline communication system are the simplicity of the approach and the use of knowledge (fuzzy IFTHEN rules and input output pairs information) about the communication medium. This paper presents a new adaptive blind equalization method based on fuzzy logic for powerline channel. We introduce a new type of fuzzy adaptive blind equalizer (FABE) using extended Kalman filter (EKF) based adaptation algorithm for powerline channel equalization. The proposed blind equalizer for powerline channel has the following merits: It is new and simple in design, and it does not requires training sequence. In a changeable distorted powerline channel, data transmission is continuous and do not stop for training the equalizer. The performance of EKF-based FABE is compared with two other types of FABEs based on the recursive least squares (RLS) and the least mean squares (LMS) adaptation algorithm. The simulation results show that EKF-based FABE has faster convergent and lower steady state probability of error compared to the other two FABEs. The bit error rate (BER) of the EKFbased FABE is close to that of the optimal equalizer.
Adaptive Neuro-Fuzzy Interference System
SpringerBriefs in Meteorology, 2015
Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin. The main aim of this research work was to determine the blood glucose level of diabetic patient using adaptive Neuro-fuzzy. Data of 80 diabetic patients were collected from Federal Medical Centre Jalingo. It was used for training and testing the system, Gaussian Membership function was used, hybrid training algorithm was used for training and testing, the error obtain is 0.0008333 at epoch 4 which shows that the training performance is exactly 99.99% and testing performance of the system are 99.99% at epoch 4. This shows that adaptive Neuro-fuzzy system can be applied to medical diagnosis because of the error obtained.
EURASIP Journal on Advances in Signal Processing, 2004
This paper introduces adaptive fuzzy equalizers with variable step size for broadband power line (PL) communications. Based on delta-bar-delta and local Lipschitz estimation updating rules, feedforward, and decision feedback approaches, we propose singleton and nonsingleton fuzzy equalizers with variable step size to cope with the intersymbol interference (ISI) effects of PL channels and the hardness of the impulse noises generated by appliances and nonlinear loads connected to low-voltage power grids. The computed results show that the convergence rates of the proposed equalizers are higher than the ones attained by the traditional adaptive fuzzy equalizers introduced by J. M. Mendel and his students. Additionally, some interesting BER curves reveal that the proposed techniques are efficient for mitigating the above-mentioned impairments.