Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response

Data telemetry is a critical element of successful unconventional well drilling operations, involving the transmission of information about the well-surrounding geology to the surface in real-time to serve as the basis for geosteering and well planning. However, the data extraction and code recovery (demodulation) process can be a complicated system due to the non-linear and time-varying characteristics of high amplitude surface noise. In this work, a novel model fuzzy wavelet neural network (FWNN) that combines the advantages of the sigmoidal logistic function, fuzzy logic, a neural network, and wavelet transform was established for the prediction of the transmitted signal code from borehole to surface with effluent quality. Moreover, the complete workflow involved the pre-processing of the dataset via an adaptive processing technique before training the network and a logistic response algorithm for acquiring the optimal parameters for the prediction of signal codes. A data reduction and subtractive scheme are employed as a pre-processing technique to better characterize the signals as eight attributes and, ultimately, reduce the computation cost. Furthermore, the frequency-time characteristics of the predicted signal are controlled by selecting an appropriate number of wavelet bases “N” and the pre-selected range for pij3 to be used prior to the training of the FWNN system. The results, leading to the prediction of the BPSK characteristics, indicate that the pre-selection of the N value and pij3 range provides a significantly accurate prediction. We validate its prediction on both synthetic and pseudo-synthetic datasets. The results indicated that the fuzzy wavelet neural network with logistic response had a high operation speed and good quality prediction, and the correspondingly trained model was more advantageous than the traditional backward propagation network in prediction accuracy. The proposed model can be used for analyzing signals with a signal-to-noise ratio lower than 1 dB effectively, which plays an important role in the electromagnetic telemetry system.

A Wireless Network Communication Capacity Control Technology Based on Fuzzy Wavelet Neural Network

The communication capacity control of the computer wireless network is the basis for realizing the efficient communication of massive data. In order to study the communication capacity control technology of the computer wireless network, improve the control effect of a large amount of data communication, and calculate the capacity of the wireless network in real time, this paper uses the fuzzy wavelet neural network to predict the wireless network channel. After the interference-free channel is obtained, the load balancing strategy of the ant colony optimization algorithm is used to filter the channel, and the channel allocation sequence with the most balanced load distribution is obtained, and a priority selection list is generated. After discretizing the channels in the largest discretization selection list, the channel sequence is allocated to the pair of nodes with communication requests according to the greedy coloring algorithm, so as to realize the communication capacity control of the computer wireless network. The test results show that the technology can guarantee good communication performance in both static and dynamic networks and can effectively complete network communication of massive data, and the communication capacity control effect is good.

Chaos synchronization using an improved type-2 fuzzy wavelet neural network with application to secure communication

In this article, a simple yet efficient adaptive control method is proposed to investigate synchronizing two chaotic systems. This approach presents an improved type-2 fuzzy wavelet neural network for estimating the unknown terms and the external disturbance in the chaotic systems’ dynamics. Furthermore, an efficient robust control term is integrated into the suggested controller so that the robustness of the controller against system unknown disturbances and uncertainties is improved. This approach offers a very desirable characteristic as a model-free controller. With the help of the Lyapunov stability theory supported with the transient performance analysis, it is established that the proposed control scheme can guarantee the synchronization and the stability of the closed-loop control system. Comparative simulation results with radial basis function neural networks are given that prove the proposed method has superiority in secure communication applications.