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Keywords = neuro-evolutionary control

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22 pages, 1066 KB  
Article
GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management
by Mingjun Kuang, Qingwen Hou, Jindong Wang, Jianping Guo and Zhengjun Wei
Machines 2025, 13(7), 624; https://doi.org/10.3390/machines13070624 - 21 Jul 2025
Viewed by 412
Abstract
This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset [...] Read more.
This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset is constructed through multi-scenario simulations using settling time, overshoot, and steady-state error as fitness metrics. The genetic algorithm (GA) facilitates broad exploration of the proportional–integral–derivative (PID) controller parameter space while ensuring control stability by discarding low-performing gain combinations. The resulting high-quality dataset is used to train the ANFIS model, enabling real-time, adaptive tuning of PID gains. Simulation results demonstrate that the proposed GA-ANFIS-PID controller significantly enhances dynamic response, robustness, and adaptability over both the conventional Ziegler–Nichols PID and GA-only PID schemes. The controller maintains stability under structural perturbations and abrupt thermal disturbances without the need for offline retuning, owing to the real-time inference capabilities of the ANFIS model. By combining global evolutionary optimization with intelligent online adaptation, this approach improves both accuracy and generalization, offering a practical and scalable solution for SPAD thermal management in demanding environments such as quantum communication, sensing, and single-photon detection platforms. Full article
(This article belongs to the Section Automation and Control Systems)
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17 pages, 3873 KB  
Article
Prediction of Post-Bath Body Temperature Using Fuzzy Inference Systems with Hydrotherapy Data
by Feng Han, Minghui Tang, Ziheng Zhang, Kenji Hirata, Yoji Okugawa, Yunosuke Matsuda, Jun Nakaya, Katsuhiko Ogasawara and Kohsuke Kudo
Healthcare 2025, 13(9), 972; https://doi.org/10.3390/healthcare13090972 - 23 Apr 2025
Viewed by 721
Abstract
Background/Objectives: Widely known for its therapeutic benefits, hydrotherapy utilizes water’s physical properties, such as temperature, hydrostatic pressure, and viscosity, to influence physiological responses. Among these, body temperature modulation plays a crucial role in enhancing circulatory function, muscle relaxation, and metabolic processes. While hydrotherapy [...] Read more.
Background/Objectives: Widely known for its therapeutic benefits, hydrotherapy utilizes water’s physical properties, such as temperature, hydrostatic pressure, and viscosity, to influence physiological responses. Among these, body temperature modulation plays a crucial role in enhancing circulatory function, muscle relaxation, and metabolic processes. While hydrotherapy can improve systemic health, particularly cardiac function, improper temperature control poses risks, especially for vulnerable populations such as the elderly or individuals with thermoregulatory impairments. Therefore, accurately predicting post-bath body temperature is essential for ensuring safety and optimizing therapeutic outcomes. Methods: This study explored the use of fuzzy inference systems to predict post-bath body temperature, leveraging an adaptive neuro-fuzzy inference system, evolutionary fuzzy inference system (EVOFIS), and enhanced Takagi-Sugeno fuzzy system. These models were compared with random forest and support vector machine models using hydrotherapy-related data. Results: The results show that EVOFIS outperformed other models, particularly in predicting deep body temperature, which is clinically significant as it is closely linked to core physiological regulation. Conclusions: The ability to accurately forecast deep-temperature dynamics enables proactive management of hyperthermia risk, supporting safer hydrotherapy practices for at-risk groups. These findings highlight the potential of FIS-based models for non-invasive temperature prediction, contributing to enhanced safety and personalization in hydrotherapy applications. Full article
(This article belongs to the Special Issue The Role of AI in Predictive and Prescriptive Healthcare)
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22 pages, 3628 KB  
Review
Beneficial Effects of Manilkara zapota-Derived Bioactive Compounds in the Epigenetic Program of Neurodevelopment
by Cristina Russo, Maria Stella Valle, Floriana D’Angeli, Sofia Surdo, Salvatore Giunta, Antonio Carlo Barbera and Lucia Malaguarnera
Nutrients 2024, 16(14), 2225; https://doi.org/10.3390/nu16142225 - 11 Jul 2024
Cited by 1 | Viewed by 2876
Abstract
Gestational diet has a long-dated effect not only on the disease risk in offspring but also on the occurrence of future neurological diseases. During ontogeny, changes in the epigenetic state that shape morphological and functional differentiation of several brain areas can affect embryonic [...] Read more.
Gestational diet has a long-dated effect not only on the disease risk in offspring but also on the occurrence of future neurological diseases. During ontogeny, changes in the epigenetic state that shape morphological and functional differentiation of several brain areas can affect embryonic fetal development. Many epigenetic mechanisms such as DNA methylation and hydroxymethylation, histone modifications, chromatin remodeling, and non-coding RNAs control brain gene expression, both in the course of neurodevelopment and in adult brain cognitive functions. Epigenetic alterations have been linked to neuro-evolutionary disorders with intellectual disability, plasticity, and memory and synaptic learning disorders. Epigenetic processes act specifically, affecting different regions based on the accessibility of chromatin and cell-specific states, facilitating the establishment of lost balance. Recent insights have underscored the interplay between epigenetic enzymes active during embryonic development and the presence of bioactive compounds, such as vitamins and polyphenols. The fruit of Manilkara zapota contains a rich array of these bioactive compounds, which are renowned for their beneficial properties for health. In this review, we delve into the action of each bioactive micronutrient found in Manilkara zapota, elucidating their roles in those epigenetic mechanisms crucial for neuronal development and programming. Through a comprehensive understanding of these interactions, we aim to shed light on potential avenues for harnessing dietary interventions to promote optimal neurodevelopment and mitigate the risk of neurological disorders. Full article
(This article belongs to the Special Issue The Effect of Phytochemical and Vitamin Adjuvants on Neurodevelopment)
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19 pages, 4979 KB  
Article
Transferability of Multi-Objective Neuro-Fuzzy Motion Controllers: Towards Cautious and Courageous Motion Behaviors in Rugged Terrains
by Adham Salih, Joseph Gabbay and Amiram Moshaiov
Mathematics 2024, 12(7), 992; https://doi.org/10.3390/math12070992 - 27 Mar 2024
Cited by 1 | Viewed by 992
Abstract
This study is motivated by the need to develop generic neuro-fuzzy motion controllers for autonomous vehicles that may traverse rugged terrains. Three types of target problems are investigated. These problems differ in terms of the expected motion behavior, including cautious, intermediate, and courageous [...] Read more.
This study is motivated by the need to develop generic neuro-fuzzy motion controllers for autonomous vehicles that may traverse rugged terrains. Three types of target problems are investigated. These problems differ in terms of the expected motion behavior, including cautious, intermediate, and courageous behaviors. The target problems are defined as evolutionary multi-objective problems aiming to evolve near optimal neuro-fuzzy controllers that can operate in a variety of scenarios. To enhance the evolution, sequential transfer optimization is considered, where each of the source problems is defined and solved as a bi-objective problem. The performed experimental study demonstrates the ability of the proposed search approach to find neuro-fuzzy controllers that produce the required motion behaviors when operating in various environments with different motion difficulties. Moreover, the results of this study substantiate the hypothesis that solutions with performances near the edges of the obtained approximated bi-objective Pareto fronts of the source problems provide better transferability as compared with those that are associated with performances near the center of the obtained fronts. Full article
(This article belongs to the Special Issue Fuzzy Logic Applications in Traffic and Transportation Engineering)
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20 pages, 6533 KB  
Article
Boosting Power Density of Proton Exchange Membrane Fuel Cell Using Artificial Intelligence and Optimization Algorithms
by Rania M. Ghoniem, Tabbi Wilberforce, Hegazy Rezk, Samer As’ad and Ali Alahmer
Membranes 2023, 13(10), 817; https://doi.org/10.3390/membranes13100817 - 28 Sep 2023
Cited by 15 | Viewed by 3457
Abstract
The adoption of Proton Exchange Membrane (PEM) fuel cells (FCs) is of great significance in diverse industries, as they provide high efficiency and environmental advantages, enabling the transition to sustainable and clean energy solutions. This study aims to enhance the output power of [...] Read more.
The adoption of Proton Exchange Membrane (PEM) fuel cells (FCs) is of great significance in diverse industries, as they provide high efficiency and environmental advantages, enabling the transition to sustainable and clean energy solutions. This study aims to enhance the output power of PEM-FCs by employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) and modern optimization algorithms. Initially, an ANFIS model is developed based on empirical data to simulate the output power density of the PEM-FC, considering factors such as pressure, relative humidity, and membrane compression. The Salp swarm algorithm (SSA) is subsequently utilized to determine the optimal values of the input control parameters. The three input control parameters of the PEM-FC are treated as decision variables during the optimization process, with the objective to maximize the output power density. During the modeling phase, the training and testing data exhibit root mean square error (RMSE) values of 0.0003 and 24.5, respectively. The coefficient of determination values for training and testing are 1.0 and 0.9598, respectively, indicating the successfulness of the modeling process. The reliability of SSA is further validated by comparing its outcomes with those obtained from particle swarm optimization (PSO), evolutionary optimization (EO), and grey wolf optimizer (GWO). Among these methods, SSA achieves the highest average power density of 716.63 mW/cm2, followed by GWO at 709.95 mW/cm2. The lowest average power density of 695.27 mW/cm2 is obtained using PSO. Full article
(This article belongs to the Special Issue Progress in Proton Exchange Membrane Fuel Cells (PEMFCs))
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34 pages, 13943 KB  
Review
Research on Synthesis of Multi-Layer Intelligent System for Optimal and Safe Control of Marine Autonomous Object
by Wojciech Koznowski, Krzysztof Kula, Agnieszka Lazarowska, Józef Lisowski, Anna Miller, Andrzej Rak, Monika Rybczak, Mostefa Mohamed-Seghir and Mirosław Tomera
Electronics 2023, 12(15), 3299; https://doi.org/10.3390/electronics12153299 - 31 Jul 2023
Cited by 7 | Viewed by 2078
Abstract
The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms—dynamic programming with [...] Read more.
The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms—dynamic programming with neural state constraints, ant colony, and neuro-phase safe control algorithms—were presented. LMI and predictive line-of-sight methods were used for optimal control. The direct control layer is implemented in multi-operations on the principle of switching. The results of the computer simulation of the algorithms were used to assess the quality control. Full article
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6 pages, 278 KB  
Proceeding Paper
Neuro-Evolutionary Synthesis of Game Models of Control under Uncertainty Based on Distributed Computing Technology
by Vladimir A. Serov, Daria L. Popova, Pavel P. Rogalev and Anastasia V. Tararina
Eng. Proc. 2023, 33(1), 59; https://doi.org/10.3390/engproc2023033059 - 25 Jul 2023
Viewed by 1179
Abstract
The methodology basic principles of the neuro-evolutionary synthesis of multi-object multi-criteria systems control models under conflict and uncertainty in real time are discussed. The proposed methodology includes the following main stages: a hierarchical optimization game model under conflict and uncertainty development; a library [...] Read more.
The methodology basic principles of the neuro-evolutionary synthesis of multi-object multi-criteria systems control models under conflict and uncertainty in real time are discussed. The proposed methodology includes the following main stages: a hierarchical optimization game model under conflict and uncertainty development; a library development of hierarchical coevolutionary algorithms for multi-criteria optimization under conflict and uncertainty; software implementation of hierarchical coevolutionary algorithms library based on distributed computing technology; and game algorithms of control under uncertainty synthesis based on the technology of neural networks ensembles. Full article
(This article belongs to the Proceedings of 15th International Conference “Intelligent Systems” (INTELS’22))
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29 pages, 15699 KB  
Article
Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone
by Manuel Mariani and Simone Fiori
Aerospace 2023, 10(5), 418; https://doi.org/10.3390/aerospace10050418 - 29 Apr 2023
Cited by 10 | Viewed by 3387
Abstract
The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, [...] Read more.
The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter’s current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function’s definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications)
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27 pages, 580 KB  
Article
Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems
by Tomasz Praczyk
Electronics 2022, 11(13), 2104; https://doi.org/10.3390/electronics11132104 - 5 Jul 2022
Cited by 7 | Viewed by 2150
Abstract
The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to [...] Read more.
The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals. Full article
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30 pages, 1677 KB  
Article
Bacterial Evolutionary Algorithm-Trained Interpolative Fuzzy System for Mobile Robot Navigation
by Ferenc Ádám Szili, János Botzheim and Balázs Nagy
Electronics 2022, 11(11), 1734; https://doi.org/10.3390/electronics11111734 - 30 May 2022
Cited by 4 | Viewed by 2158
Abstract
This paper describes the process of building a transport logic that enables a mobile robot to travel fast enough to reach a desired destination in time, but safe enough to prevent damage. This transport logic is based on fuzzy logic inference using fuzzy [...] Read more.
This paper describes the process of building a transport logic that enables a mobile robot to travel fast enough to reach a desired destination in time, but safe enough to prevent damage. This transport logic is based on fuzzy logic inference using fuzzy rule interpolation, which allows for accurate inferences even when using a smaller rule base. The construction of the fuzzy rule base can be conducted experimentally, but there are also solutions for automatic construction. One of them is the bacterial evolutionary algorithm, which is used in this application. This algorithm is based on the theory of bacterial evolution and is very well-suited to solving optimization problems. Successful transport is also facilitated by proper path planning, and for this purpose, the so-called neuro-activity-based path planning has been used. This path-planning algorithm is combined with interpolative fuzzy logic-based speed control of the mobile robot. By applying the described methods, an intelligent transport logic can be constructed. These methods are tested in a simulated environment and several results are investigated. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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25 pages, 3141 KB  
Article
Neuroevolutionary Control for Autonomous Soaring
by Eric J. Kim and Ruben E. Perez
Aerospace 2021, 8(9), 267; https://doi.org/10.3390/aerospace8090267 - 17 Sep 2021
Cited by 15 | Viewed by 3548
Abstract
The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind [...] Read more.
The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the topology of such networks is usually predetermined, restricting the search space of potential solutions, while often resulting in complex neural networks that can pose implementation challenges for the limited hardware onboard small-scale autonomous vehicles. In exploring a novel method of generating neurocontrollers, this paper presents a neural network-based soaring strategy to extend flight times and advance the potential operational capability of SUAVs. In this study, the Neuroevolution of Augmenting Topologies (NEAT) algorithm is used to train efficient and effective neurocontrollers that can control a simulated aircraft along sustained dynamic and thermal soaring trajectories. The proposed approach evolves interpretable neural networks in a way that preserves simplicity while maximizing performance without requiring extensive training datasets. As a result, the combined trajectory planning and aircraft control strategy is suitable for real-time implementation on SUAV platforms. Full article
(This article belongs to the Special Issue Energy Efficiency of Small-Scale UAVs)
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15 pages, 3195 KB  
Article
A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems
by Bin Zhang and Yung C. Shin
Appl. Sci. 2021, 11(1), 62; https://doi.org/10.3390/app11010062 - 23 Dec 2020
Cited by 12 | Viewed by 4755
Abstract
A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear [...] Read more.
A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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24 pages, 10269 KB  
Article
A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network
by Marcin Bernas, Bartłomiej Płaczek and Jarosław Smyła
Sensors 2019, 19(8), 1776; https://doi.org/10.3390/s19081776 - 13 Apr 2019
Cited by 13 | Viewed by 3410
Abstract
The paper introduces an artificial neural network ensemble for decentralized control of traffic signals based on data from sensor network. According to the decentralized approach, traffic signals at each intersection are controlled independently using real-time data obtained from sensor nodes installed along traffic [...] Read more.
The paper introduces an artificial neural network ensemble for decentralized control of traffic signals based on data from sensor network. According to the decentralized approach, traffic signals at each intersection are controlled independently using real-time data obtained from sensor nodes installed along traffic lanes. In the proposed ensemble, a neural network, which reflects design of signalized intersection, is combined with fully connected neural networks to enable evaluation of signal group priorities. Based on the evaluated priorities, control decisions are taken about switching traffic signals. A neuroevolution strategy is used to optimize configuration of the introduced neural network ensemble. The proposed solution was compared against state-of-the-art decentralized traffic control algorithms during extensive simulation experiments. The experiments confirmed that the proposed solution provides better results in terms of reduced vehicle delay, shorter travel time, and increased average velocity of vehicles. Full article
(This article belongs to the Section Sensor Networks)
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