4 research outputs found
Adaptive control strategy for velocity control of a linear switched reluctance motor
A new adaptive control strategy for a double-sided linear switched reluctance motor is proposed in this work. Switched reluctance motors inherently have non-linear dynamics that complicates their analysis and control. The Takagi-Sugeno fuzzy logic system has been used in this work to achieve an appropriate adaptive control system for the proposed linear motor. The fuzzy system has been heightened by non-linear rule consequences and a time-varying parameter. Updated laws have been designed to refresh the amount of the parameter consequently the suggested control system shows a stable performance in control of switched reluctance motors. The proposed control strategy is independent of the uncertainties of the motor dynamics and has an acceptable adaptability. This control method along with a conventional fuzzy logic based system have been studied on the proposed linear switched reluctance motor and their performances were compared in different performance conditions. An appropriate evaluation method has been used to compare the obtained results. The efficiency of the proposed method in different velocity commands has been examined and confirmed using appropriate simulation and experimental results. The Institution of Engineering and Technology 2020Scopus2-s2.0-8508983303
Deep Learning-Based Machinery Fault Diagnostics
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis
Gene expression programming for Efficient Time-series Financial Forecasting
Stock market prediction is of immense interest to trading companies and buyers due to
high profit margins. The majority of successful buying or selling activities occur close
to stock price turning trends. This makes the prediction of stock indices and analysis a
crucial factor in the determination that whether the stocks will increase or decrease the
next day. Additionally, precise prediction of the measure of increase or decrease of
stock prices also plays an important role in buying/selling activities. This research
presents two core aspects of stock-market prediction. Firstly, it presents a Networkbased
Fuzzy Inference System (ANFIS) methodology to integrate the capabilities of
neural networks with that of fuzzy logic. A specialised extension to this technique is
known as the genetic programming (GP) and gene expression programming (GEP) to
explore and investigate the outcome of the GEP criteria on the stock market price
prediction.
The research presented in this thesis aims at the modelling and prediction of short-tomedium
term stock value fluctuations in the market via genetically tuned stock market
parameters. The technique uses hierarchically defined GP and gene-expressionprogramming
(GEP) techniques to tune algebraic functions representing the fittest
equation for stock market activities. The technology achieves novelty by proposing a
fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance
between varied mutation rates between varied-fitness chromosomes thereby improving
prediction accuracy and fitness improvement rate. The methodology is evaluated
against five stock market companies with each having its own trading circumstances
during the past 20+ years. The proposed GEP/GP methodologies were evaluated based
on variable window/population sizes, selection methods, and Elitism, Rank and Roulette
selection methods. The Elitism-based approach showed promising results with a low
error-rate in the resultant pattern matching with an overall accuracy of 95.96% for
short-term 5-day and 95.35% for medium-term 56-day trading periods. The
contribution of this research to theory is that it presented a novel evolutionary
methodology with modified selection operators for the prediction of stock exchange
data via Gene expression programming. The methodology dynamically adapts the
mutation rate of different fitness groups in each generation to ensure a diversification
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balance between high and low fitness solutions. The GEP-FAMR approach was
preferred to Neural and Fuzzy approaches because it can address well-reported
problems of over-fitting, algorithmic black-boxing, and data-snooping issues via GP
and GEP algorithmsSaudi Cultural Burea