Adaptive Neuro-fuzzy Inference System Research Articles

Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model

This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset’s preprocessing involved decomposing the date into year, month, and day components to enhance the model’s input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week’s prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model’s accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model’s insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model’s predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry.

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon

Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.

Prediction and optimization kerf width in laser beam machining of titanium alloy using genetic algorithm tuned adaptive neuro-fuzzy inference system

Prediction and optimization kerf width in laser beam machining of titanium alloy using genetic algorithm tuned adaptive neuro-fuzzy inference system

A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting

Accurate wind power forecasting is essential for (i) the management of wind energy, (ii) increasing the integration of generated power into the electrical grid, and (iii) enhancing maintenance efficiency. This paper proposes a novel hybrid model for short-term (1-hour ahead) wind power forecasting. The model integrates the echo-state network (ESN) architecture with empirical mode decomposition (EMD) to improve forecast accuracy. Unlike existing approaches that use separate models for each decomposed signal after EMD method, proposed model uses a single ESN structure to predict wind power by incorporating all decomposed signals and their past values. The main advantage of this architecture is that it eliminates the need to train multiple models, thereby streamlining the forecasting process. To evaluate the performance of the proposed model, one year of data from the West of Duddon Sands, Barrow, and Horns Power offshore wind farms is utilized. Firstly, the classical ESN model and the EMD-ESN hybrid model are compared for the three datasets. Then, a comprehensive evaluation is performed by comparing the results of the proposed model with commonly used standalone and hybrid forecasting models such as Multi-Layer Perceptron (MLP), Adaptive-Neuro Fuzzy Inference System (ANFIS), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), EMD-LSTM, EMD-BiLSTM, Variational Mode Decomposition based ESN (VMD-ESN), and Wavelet Decomposition based ESN (WD-ESN). The results show that the EMD-ESN hybrid model outperforms implemented models in predicting wind power in all three datasets. Furthermore, the proposed EMD-ESN model for an onshore wind turbine power data in Germany is compared with the SOTA models, such as Transformer, Informer, Autoformer, and Graph Patch Informer (GPI), in the literature. This study highlights the superior predictive capabilities of proposed model, making it a valuable tool for enhancing the accuracy of wind power forecasts, thereby contributing to the reliable integration of wind energy into the power grid.

Enhancing Control Over Nitric Oxide Photorelease via a Molecular Keypad Lock.

Based on Boolean logic, molecular keypad locks secure molecular information, typically with an optical output. Here we investigate a rare example of a molecular keypad lock with a chemical output. To this end, the light-activated release of biologically important nitric oxide from a ruthenium complex is studied, using proton concentration and photon flux as inputs. In a pH-dependent equilibrium, a nitritoruthenium(II) complex is turned into a nitrosylruthenium(II) complex, which releases nitric oxide under irradiation with visible light. The precise prediction of the output nitric oxide concentration as function of the pH and photon flux is achieved with an artificial intelligence approach, namely the adaptive neuro-fuzzy inference system. In this manner an exceptionally high level of control over the output concentration is obtained. Moreover, the provided concept to lock a chemical output as well as the output prediction may be applied to other (photo)release schemes.

Precision in Insurance Forecasting: Enhancing Potential with Ensemble and Combination Models based on the Adaptive Neuro-Fuzzy Inference System in the Egyptian Insurance Industry

ABSTRACT Enhancing the precision of retention ratio predictions holds profound significance for insurance industry decision-makers and those vested in advancing insurance services. Precision helps insurance companies navigate inflationary pressures and evaluate underwriting profitability, enabling reliable prognoses of future underwriting gains. As far as we know, although there have been multiple attempts to construct a predictive model for retention ratio, none of these attempts have used combining models or studied the Egyptian market. Therefore, this study contributes significantly to this developing field by providing combining models, which combined statistical time series models such as Exponential Smoothing (ES), and Autoregressive Integrated Moving Average (ARIMA), with Adaptive Neuro-Fuzzy Inference System (ANFIS). Two different types of combinations are employed with these models. Furthermore, the study introduces three ensemble models designed for the purpose of predicting the retention ratio within the Egyptian insurance market. Dataset was carefully gathered from the EFSA’s annual reports, focused on the property-liability insurance sector within the Egyptian insurance market and covers the time period from 1989 to 2021. Next, the proposed models are assessed employing well-established statistical assessment metrics, namely, Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), R Square (R2), and Root Mean Square Error (RMSE). The results show that combining and ensemble methods improve predicted accuracy. A multi-linear regression-based ensemble model that combines ARIMA, ES, and ANFIS models outperforms both single and combined models in robustness. The article concludes that the insurance industry can greatly benefit from modern predictive methods to make sound decisions.

Artificial intelligence-based prediction models of bio-treated sand strength for sustainable and green infrastructure applications

As a sustainable, environmentally friendly, and green solution, biomineralization/bio-treatment process has recently emerged for enhancing the strength characteristics of loose sands by inducing cementation material commonly referred to as microbially induced calcite/cementation precipitation (MICP). To date, existing research on the unconfined compression strength of MICP-treated sands (UCSmicp) relies heavily on experimental data, with predictions primarily based on linear or curvilinear models that consider calcite (CaCO3) as the main influencing factor. However, there is limited exploration into the application of artificial intelligence (AI) and machine learning (ML) methods for UCSmicp prediction involving multiple influencing factors and their validation on unseen laboratory data. Therefore, this study intends to propose new prediction models of UCSmicp using four advanced ML techniques: Gaussian Process Regression (GPR), Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Gene Expression Programming (GEP). The models incorporate various factors affecting UCSmicp, including sand uniformity coefficient (Cu), initial void ratio (e), mean grain size (D50), optical density at 600 mm wavelength (OD600), urea concentration (U), calcium concentration (Ca), and calcite content (C). Performance evaluation of models involved statistical assessments, error analysis, cross validation on laboratory data, and sensitivity and monotonicity studies. Results indicate that the proposed models exhibit exceptional precision across training, testing, and validation data in predicting UCSmicp, with ANN demonstrating the highest accuracy, followed by GPR, ANFIS, and GEP. Sensitivity analysis identifies CaCO3 as the main influencing factor on UCSmicp, while monotonicity analysis confirms alignment with physical processes. This work advocates for MICP as an eco-friendly solution and highlights the efficacy of advanced ML techniques in fostering sustainable infrastructure.

Enhanced adsorption of Bismark Brown R dye by chitosan conjugated magnetic pectin loaded filter mud: A comprehensive study on modeling and mechanisms

Herein, a polymer-based bioadsorbent was prepared by cross-linking chitosan to filter mud and magnetic pectin (Ch-mPC@FM) for the removal of Bismark Brown R dye (BB-R) from wastewater. Morphological characterization analysis indicated that Ch-mPC@FM had a higher surface area and better pore structure than its components. The Artificial Neuron Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to evaluate the simulation and prediction of the adsorption process based on input variables like temperature, pH, dosage, initial BB-R dye concentration, and contact time. ANFIS and ANN demonstrated significant modeling and predictive accuracy, with R2 > 0.93 and R2 > 0.96, and root mean square error < 0.023 and <0.020, respectively. The Langmuir isotherm and the pseudo-second-order kinetic models provided the best fits to the equilibrium and kinetic data. The thermodynamic assessment showed spontaneous and endothermic adsorption with average entropy and enthalpy changes of 119.32 kJ mol−1 K and 403.47 kJ mol−1, respectively. The study of BB-R dye adsorption on Ch-mPC@FM revealed multiple mechanisms, including electrostatic, complexation, pore filling, cation-π interaction, hydrogen bonding, and π-π interactions. The approximate production cost of US$ 5.809 Kg−1 and excellent adsorption capability render Ch-mPC@FM an inexpensive, pragmatic, and ecologically safe bioadsorbent for BB-R dye removal from wastewater.

Advancements in predicting scour depth induced by turbulent wall jets: A comparative analysis of mathematical formulations and machine learning models

This study examines the scour depth induced by turbulent wall jets and proposes novel mathematical formulations to predict the depth of scouring. Through a comprehensive gamma test, key parameters influencing the scour depth are identified, including the apron length, densimetric Froude number, median sediment size, tailwater level, Reynolds number, and Froude number of the jet. Regression analysis is subsequently conducted to establish relationships between the dependent parameter and the aforementioned independent variables. A comparative analysis is then undertaken between the measured scour depths and those predicted by existing equations from previous studies. Furthermore, predictive models leveraging the support vector machine, artificial neural network with particle swarm optimization, M5 tree algorithm, gene expression programming, and adaptive neuro-fuzzy inference system (ANFIS) are developed using the collected data. Statistical metrics are employed to evaluate the performance of each model and the regression equation. The effectiveness of each model in predicting scour depth is demonstrated. Notably, ANFIS yields a coefficient of determination of 0.809 and a root mean square error (RMSE) of 1.585. Multi-nonlinear regression analysis exhibits a coefficient of determination of 0.752 and an RMSE of 0.421, while the M5 tree achieves a coefficient of determination of 0.739 and an RMSE of 1.874, demonstrating superior performance compared to other machine learning techniques and regression equations employed in this study.

Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.

Developing an Adaptive Neuro-Fuzzy Inference System for Performance Evaluation of Pavement Construction Projects

This study employs an adaptive neuro-fuzzy inference system (ANFIS) to identify critical success factors (CSFs) crucial for the success of pavement construction projects. Challenges such as construction cost delays, budget overruns, disputes, claims, and productivity losses underscore the need for effective project management in pavement projects. In contemporary construction management, additional performance criteria play a vital role in influencing the performance and success of pavement projects during construction operations. This research contributes to the existing body of knowledge by comprehensively identifying a multidimensional set of critical success performance factors that impact pavement and utility project management. A rigorous literature review and consultations with pavement experts identified sixty CSFs, categorized into seven groups. The relative importance of each element and group is determined through the input of 287 pavement construction specialists who participated in an online questionnaire. Subsequently, the collected data undergo thorough checks for normality, dependability, and independence before undergoing analysis using the relative importance index (RII). An ANFIS is developed to quantitatively model critical success factors and assess the implementation performance of construction operations management (COM) in the construction industry, considering aspects such as clustering input/output datasets, fuzziness degree, and optimizing five Gaussian membership functions. The study confirms the significance of three primary CSFs (financial, bureaucratic, and governmental) and communication-related variables through a qualitative structural and behavioral validation process, specifically k-fold cross-validation. The outcomes of this research hold practical implications for the management and assessment of overall performance indices in pavement construction projects. The ANFIS model, validated through robust testing methodologies, provides a valuable tool for industry professionals seeking to enhance the success and efficiency of pavement construction endeavors.

Intelligence modeling of the flow boiling heat transfer of nanorefrigerant for integrated energy system

To promote the application of nanorefrigerant in Organic Rankine Cycle and Integrated Energy System a reliable model with simple structure and favorable accuracy for predicting the flow boiling heat transfer coefficient (HTC) of nanorefrigerant is essential. In this work, four intelligence models—the radial basis function (RBF), multilayer perceptron (MLP), least squares support vector machine (LSSVM), and adaptive neuro fuzzy inference system (ANFIS)—were developed to predict the flow boiling heat transfer coefficient using nanorefrigerants, based on 765 experimental samples. The performances of these artificial intelligence models were comprehensively evaluated through accuracy analysis, variation trend analysis, and sensitivity analysis. Results indicated that the comprehensive performance of the RBF model was superior than those of other intelligence models and the existing empirical models. The RBF model accurately captured the variation trend of the output as the input variables were varied. Meanwhile, the impact degrees of all input variables in decreasing order were nanoparticle concentration (φ), mass flux (G), thermal conductivity of nanoparticle (kp), and vapor quality (x).

Implementation of adaptive neuro-fuzzy inference system in design and process optimization of latent heat storage system

This study focuses on implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an artificial neural network model to optimize latent heat thermal energy storage (LHTES) systems. Experimental data comprising seven input parameters (length, shell diameter, tube diameter, mass flow rate, difference in temperature, latent heat, and density) and two output parameters (charging and partial charging time) are collected and used to train the ANFIS model. Initially, the model is developed using a grid partition method, employing Triangular and Gbell membership functions and assigning diverse membership rules. This initial model demonstrated satisfactory accuracy based on quality metrics (R2, mean absolute, and root mean square error). Then, a subtractive clustering approach is applied to further enhance the model's accuracy, which groups different data clusters together. This enhanced model exhibited superior performance compared to the grid partition method, making it the optimal choice. In addition, an experimentally validated numerical model is developed using COMSOL Multiphysics and is utilized to crosscheck the predicted output of the ANFIS model. From a set of 100 combinations of storage capacity between 0.45 and 0.55 MJ, a fixed latent heat storage capacity of 0.456 MJ was identified by considering the combination with the least charging time based on the input parameters. Using this storage capacity, a minimum charging time of 223 min and a partial charging time of 180 min was determined. The numerical model also confirmed these optimal values.

Bitcoin Price Prediction with Fuzzy Logic

Due to cryptocurrencies' rising prices, like bitcoin, more and more people are becoming interested in them. Success in this business depends on a good price prediction. Several methods, including heuristic and machine-learning-based ones, can currently estimate the price with varied degrees of success. This study will use the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model to predict the price's general direction over the next 10 days. Along with popular traders' indicators, the previous day's price will be used. The findings demonstrated that, despite errors, price direction predictions—an increase, a drop, or a stable price—are typically accurate.

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise thestrategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall riskof IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio fromhistorical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned.

An approach for flood flow prediction utilizing new hybrids of ANFIS with several optimization techniques: a case study

Abstract Using machine learning methods is efficient in predicting floods in areas where complete data are not available. Therefore, this study considers the Adaptive Neuro-Fuzzy Inference System (ANFIS) model combined with evolutionary algorithms, namely Harris Hawks Optimization (HHO) and Arithmetic Optimization Algorithm (AOA), to predict the flood of the Shahrchay River in the northwest of Iran. The data used included the daily data of precipitation, evaporation, and runoff in the years 2016 and 2017, where 70% of the data were used for model training and 30% for testing the models. The results showed that although the ANFIS model provided values with high errors in several steps, especially in estimating steps with maximum or minimum values, the use of HHO and AOA resulted in a significant reduction in the error values. The ANFIS–AOA model utilizing an input scenario including the flow in the previous 1–3 days exerted the most promising results in the test data, with the Nash–Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) of 0.93, 1.34, and 0.69, respectively. According to Taylor's diagram, the ANFIS–AOA hybrid algorithm predicts flood values with greater performance than the other models.

Experimental Evaluation of Nano Coating on the Draft Force of Tillage Implements and Its Prediction Using an Adaptive Neuro-Fuzzy Inference System (ANFIS)

The effect of coating a flat blade surface with titanium nitride nano coatings (TiN), nano tantalum carbide (TaC), Fiberglass (Glass Fiber-Reinforced Polymer) (GFRP), Galvanized Steel (GAS), and St37 (SST37) was investigated in order to decrease the adhesion of soil on tilling tools, external friction and, ultimately, the draft force. The soil tank, which was filled with soil of the desired conditions, was pulled on the bearing on the rail. A S-shaped load cell was used to measure the draft force. Tests were conducted at a distance of 2 m and speeds of 0.1, 0.2, and 0.3 m·s−1 at a depth of 10 cm. A model based on input factors, including blade travel speed, rake angle, and cohesion and adhesion of soil–blade, was developed in an adaptive neuro-fuzzy inference system (ANFIS), and draft force was the output parameter. To verify the performance of the developed model using ANFIS, a relative error(ε) of 6.1% and coefficient of determination (R2) of 0.956 were computed. It was found that blades coated with Nano (TiN-TaC), due to its hydrophobic surface, flatness, and self-cleaning properties, have considerable ability to decrease adhesion in wet soils and showed a linear relationship with draft force reduction.

Estimation single output with a hybrid of ANFIS and MOPSO_HS

In the field of soft computing, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been more well-liked in recent years for its predictive capabilities. Appropriate ANFIS parameter adjusting is critical, which creates a gap in its predictive integration with traditional optimization techniques. Although some academics have concentrated on incorporating single-objective optimization, they frequently encounter issues with reliability and stability when striving to solve problems. In this work, an innovative multi-objective optimization technique that integrates ANFIS with MOPSO_HS is introduced. The model has consistency in problem solving and shows accurate predictions for both odd and even interval input models. In addition, three actual datasets are used to demonstrate the effectiveness of the suggested model's integration. A comparison is made between the suggested integrated model and established algorithms after 20 runs of analysis. The algorithm's accuracy, stability, and dependability in resolving integration problems are demonstrated by the results, which also show how superior it is to alternative approaches.

Pitch motion suppression of electric vehicle active suspensions based on multibody dynamics

Active suspension control strategies are proposed to suppress pitch motion and address the issue of motion sickness among electric vehicle passengers. In this work, we investigate the performance of the linear quadratic regulator (LQR), variable universe fuzzy control, adaptive-network-based fuzzy inference system (ANFIS), and fuzzy PID control under continuous acceleration and deceleration and bumpy road conditions. First, a 14-degrees-of-freedom (DOF) dynamics model of an electric vehicle is developed based on a semirecursive multibody dynamics method, which was originally proposed by Javier García de Jalón. The vehicle dynamics simulation is performed to accurately model the vehicle states and responses. Second, the LQR, variable universe fuzzy control, ANFIS, and Fuzzy PID control algorithms are tailored via vehicle states, including pitch, rate, and acceleration. Their performances are investigated and compared in detail. Finally, the robustness of the proposed control strategies is further discussed based on various speed bump parameters and initial velocities. The results indicate that the proposed control strategies are capable of suppressing the pitch motion of electric vehicles, enhancing the riding comfort under diverse operating conditions, thereby reducing the likelihood of motion sickness among passengers of electric vehicles.