The integration of fuzzy systems and neural networks has recently become a popular approach in engineering fields for modelling and control of uncertain systems. This paper presents the development of novel type-2 neuro-fuzzy system for identification of time-varying systems and equalization of time-varying channels using clustering and gradient algorithms. It combines the advantages of type-2 fuzzy systems and neural networks. The type-2 fuzzy system allows handling the uncertainties associated with information or data in the knowledge base of the process. The structure of the proposed type-2 TSK fuzzy neural system (FNS) is given and its parameter update rule is derived, based on fuzzy clustering and gradient learning algorithm. The proposed structure is used for identification and noise equalization of time-varying systems. The effectiveness of the proposed system is evaluated by comparing the results obtained by the use of models seen in the literature.

A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization

A self-constructing fuzzy neural network (SCFNN) has been successfully used for chaotic time series prediction in the literature. In this paper, we propose the strategy of adding a recurrent path in each node of the hidden layer of SCFNN, resulting in a self-constructing recurrent fuzzy neural network (SCRFNN). This novel network does not increase complexity in fuzzy inference or learning process. Specifically, the structure learning is based on partition of the input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. This novel network can also be applied for chaotic time series prediction including Logistic and Henon time series. More significantly, it features rapider convergence and higher prediction accuracy.

/pdf/a-new-approach-for-chaotic-time-series-prediction-using-4pnzc4zr1y.pdf

A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network

This study investigates the use of error control code, discrete wavelet transform (DWT) and artificial neural network (ANN) to improve the link performance of an indoor optical wireless communication in a physical channel. The key constraints that barricade the realization of unlimited bandwidth in optical wavelengths are the eye-safety issue, the ambient light interference and the multipath induced intersymbol interference (ISI). Eye-safety limits the maximum average transmitted optical power. The rational solution is to use power efficient modulation techniques. Further reduction in transmitted power can be achieved using error control coding. A mathematical analysis of retransmission scheme is investigated for variable length modulation techniques and verified using computer simulations. Though the retransmission scheme is simple to implement, the shortfall in terms of reduced throughput will limit higher code gain. Due to practical limitation, the block code cannot be applied to the variable length modulation techniques and hence the convolutional code is the only possible option. The upper bound for slot error probability of the convolutional coded dual header pulse interval modulation (DH-PIM) and digital pulse interval modulation (DPIM) schemes are calculated and verified using simulations. The power penalty due to fluorescent light interference (FL I) is very high in indoor optical channel making the optical link practically infeasible. A denoising method based on a DWT to remove the FLI from the received signal is devised. The received signal is first decomposed into different DWT levels; the FLI is then removed from the signal before reconstructing the signal. A significant reduction in the power penalty is observed using DWT. Comparative study of DWT based denoising scheme with that of the high pass filter (HPF) show that DWT not only can match the best performance obtain using a HPF, but also offers a reduced complexity and design simplicity. The high power penalty due to multipath induced ISI makes a diffuse optical link practically infeasible at higher data rates. An ANN based linear and DF architectures are investigated to compensation the ISI. Unlike the unequalized cases, the equalized schemes don‘t show infinite power penalty and a significant performance improvement is observed for all modulation schemes. The comparative studies substantiate that ANN based equalizers match the performance of the traditional equalizers for all channel conditions with a reduced training data sequence. The study of the combined effect of the FLI and ISI shows that DWT-ANN based receiver perform equally well in the present of both interference. Adaptive decoding of error control code can offer flexibility of selecting the best possible encoder in a given environment. A suboptimal ?soft‘ sliding block convolutional decoder based on the ANN and a 1/2 rate convolutional code with a constraint length is investigated. Results show that the ANN decoder can match the performance of optimal Viterbi decoder for hard decision decoding but with slightly inferior performance compared to soft decision decoding. This provides a foundation for further investigation of the ANN decoder for convolutional code with higher constraint length values. Finally, the proposed DWT-ANN receiver is practically realized in digital signal processing (DSP) board. The output from the DSP board is compared with the computer simulations and found that the difference is marginal. However, the difference in results doesn‘t affect the overall error probability and identical error probability is obtained for DSP output and computer simulations.

Application of wavelets and artificial neural network for indoor optical wireless communication systems

Neural networks have recently been used in blind equalization extensively due to their capability to discover complex decision regions. This paper introduces a novel approach to adaptive channel equalization with recurrent neural network (RNN) for a QSPK signal constellation. The proposed method utilises an FIR based natural gradient in conjunction with a scale factor to update the weights. The use of the natural gradient in RNN for weight update is two-fold: stabilizing the weights without normalization and establishing the network's capability to perform blind equalization. The work targets wireless communications in non linear channels for M-PSK and M-QAM modulation schemes. Computer simulations show that the natural gradient offers a stable training to RNN, where the weights are small in size and vary slowly with time.

Blind equalization with recurrent neural networks using natural gradient

Channel equalization is a major method for reducing distortion and interference effects on a communication channel. In this paper, channel equalization using soft computing methods is attempted. To be more specific, Dynamic Fuzzy Neural Networks (DFNN) which combines fuzzy rules and neural networks is adopted. The DFNN is functionally equivalent to a Takagi-Sugeno-Kang (TSK) fuzzy system possessing the learning ability of a Radial Basis Function (RBF) neural network. The hidden neurons (rules) of the DFNN can be added and pruned dynamically during the training process based on the significance of each neuron to achieve a compact topology structure. Simulation studies demonstrate that the performance of the DFNN-based equalizer is superior to some other existing equalizers in terms of Bit Error Rate (BER).

Channel Equalization Using Dynamic Fuzzy Neural Networks

The design of a self-constructing fuzzy neural network (SCFNN)-based digital channel equalizer is proposed in this paper. We demonstrate that the SCFNN-based digital channel equalizer possesses the ability to recover the channel distortion effectively. The performance of SCFNN is compared with that of the adaptive-based-network fuzzy inference system (ANFIS) and the optimal Bayesian solution. Simulations were carried out in both real-valued and complex-valued nonlinear channels to demonstrate the flexibility of the proposed equalizer. The experimental results show that the performance of SCFNN can be close to that of the Bayesian optimal solution and ANFIS, while the hardware requirement of the trained SCFNN-based equalizer is much lower.

The Design of an SCFNN Based Nonlinear Channel Equalizer

In this paper the design of a self‐constructing recurrent fuzzy neural network (SCRFNN)‐based digital channel equalizer is proposed. It is found that a digital channel equalizer based on SCRFNN can recover channel distortions effectively. We compare the performance of SCRFNN with adaptive‐based‐network fuzzy inference system (ANFIS) and the Bayesian equalizers in complex‐valued linear channels. Our simulations show that the performance of SCRFNN is close to the Bayesian optimal solution. Furthermore, the hardware requirement of the trained SCRFNN equalizer is relatively lower than the other two structures.

Using self‐constructing recurrent fuzzy neural network as a digital channel equalizer

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The Design of an SCFNN Based Nonlinear Channel Equalizer

Using self‐constructing recurrent fuzzy neural network as a digital channel equalizer