Fuzzy-based Neural Network Research Articles

Research on Mental Health Assessment and Intervention Methods for College Students based on Big Data Analysis

This study introduces the novel Fuzzy-Enhanced Predictive Neural System for Student Mental Health (FEPS-MH) approach to mental health assessment and intervention for college students. FEPS-MH synergistically combines a Backpropagation (BP) Neural Network with a Deep Fuzzy-Based Neural Network (DFBNN), leveraging the strengths of both systems to handle the complexities of mental health data in the context of big data analytics. The BP Neural Network, known for its effective learning and generalization capabilities, is integrated with the DFBNN to process imprecise, uncertain, or subjective data, typical in mental health assessments. The core objective of FEPS-MH is to provide a more accurate, robust, and sensitive analysis of mental health states, incorporating the nuanced variations and uncertainties inherent in psychological data. This system is designed to analyze a vast array of data sources, including but not limited to, behavioral patterns, self-reported questionnaires, and social media interactions, to identify potential mental health issues among college students. FEPS-MH’s capabilities extend beyond mere assessment; it is also equipped to recommend personalized intervention strategies. Utilizing big data analysis, the system not only predicts potential mental health crises but also suggests tailored intervention approaches based on the unique psychological profile of each student. This study demonstrates the feasibility and effectiveness of FEPS-MH through a series of tests and validations using real-world data. The results indicate a significant improvement in both the accuracy of mental health assessments and the efficacy of suggested interventions. FEPS-MH stands as a promising tool for educational institutions, offering a data-driven, sensitive, and comprehensive approach to student mental health care. Its implementation could revolutionize the field of mental health support in college environments, making it a vital asset for proactive psychological wellness in educational settings.

Multi-Parameter Fuzzy-Based Neural Network Sensorless PMSM Iterative Learning Control Algorithm for Vibration Suppression of Ship Rim-Driven Thruster

Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which the sliding mode coefficients can be adaptively tuned. Similarly, an iterative learning control (ILC) algorithm is presented to enhance the robustness of the velocity adjustment loop. By continuously learning and adjusting the difference between the actual speed and given speed of RDT motor through ILC algorithm, online compensation for the q-axis given current of RDT motor is achieved, thereby suppressing periodic speed fluctuations during motor running. Fuzzy neural network (FNN) training can be used to optimize the STA-SMO and ILC parameters of RDT control system, while improving speed tracking accuracy. Finally, simulation and experimental verifications have been conducted on the vector control system based on the conventional PI-STA and modified ILC-STA. The results show that the modified algorithm can effectively suppress the estimated speed and torque ripple of RDT motor, which greatly improves the speed tracking accuracy.

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

We looked at the background of fault-detection and fault-tolerant control algorithms to propose a new high efficiency one with a focus on Tennessee Eastman process through fuzzy-based neural network representation. Due to the fact that the open-loop system may not be stabilized, an advanced control strategy to generate proper control signals needs to be designed. At first, to detect and identify the fault, data preprocessing theories have been considered. Based upon the matter disclosed, to provide a reliable decision-maker block, fusion classifier idea has been realized. For this one, raw data, time, and frequency characteristics are divided into various classification tools and finally the obtained knowledge combination regarding each one of them is adopted. It should be noted that the proposed implementation tools are taken into real consideration as the fuzzy-based neural network representation. Subsequently, the fault-tolerant control approach based on local controller regulation in case of each fault occurrence has been researched, which the investigated outcomes emphasize the effectiveness of the approach proposed here.

Controlling disturbances of islanding in a gas power plant via fuzzy-based neural network approach with a focus on load-shedding system

In a number of power plants such as the South Pars Gas Company, efficient control performance regarding the turbine generator is always needed to avoid facing disturbances of islanding in the supply of power, at each instance of time. It is particularly important to deal with through state-of-the-art techniques, due to the fact that the separation of the electrical grid may increase or reduce the speed of the above-referenced turbine generator and correspondingly the frequency, in its permitted range, may be deviated to stop power generation, abruptly. With this key goal, a set of control strategies through the representation of the gas turbine via Rowen is proposed in the present research. In a word, there are some classical and intelligence-based control approaches to cope with the disturbance of islanding with a focus on load-shedding system. In reality, the proposed investigation is mainly concentrating on the fuzzy-based neural network control approach to cope with the turbulence of frequency deviation. The main contribution made in this research is to deal with gas turbine model in association with generator in conjunction with load-shedding system, while intelligence-based techniques considering fuzzy, neural network and genetic algorithm are synchronously established to design the control strategy with high accuracy. In the sequel, the effectiveness of the proposed intelligence-based control approach is verified, while the classical proportional-integral-derivative control approach via the Ziegler–Nichols and also the simulated annealing tuning processes are taken as the benchmarks.

Multi-criteria fuzzy decision support for conceptual evaluation in design of mechatronic systems: a quadrotor design case study

Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.

Health monitoring of vibration signatures in rotorcraft wings

We propose to design and to evaluate an on-board intelligent health assessment tool for rotorcraft machines, which is capable of detecting, identifying, and accommodating expected system degradations and unanticipated catastrophic failures in rotorcraft machines under an adverse operating environment. A fuzzy-based neural network paradigm with an on-line learning algorithm is developed to perform expert advising for the ground-based maintenance crew. A hierarchical fault diagnosis architecture is advocated to fulfill the time-critical and on-board needs in different levels of structural integrity over a global operating envelope. The research objective is to experimentally demonstrate the feasibility and flexibility of the proposed health monitoring procedure through numerical simulations of bearing faults in USAF MH-53J PAVELOW helicopter transmissions. The proposed fault detection, identification and accommodation architecture is applicable to various generic rotorcraft machines. The proposed system will greatly reduce the operational and developmental costs and serve as an essential component in an autonomous control system.