Machine Learning for Identification and Optimal Control of Advanced Automotive Engines.
Explore this paper's citation graph
Summary
The modeling and control problem of an advanced automotive engine, the homogeneous charge compression ignition (HCCI) engine, is addressed using data based learning techniques.
- Type
- dissertation
- Published
- 2013-01-01
- Cited by
- 14
- References
- 104
- Access
- Open access
- OpenAlex
- https://openalex.org/W122396070
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:107364409
Keywords
Automotive industry, Identification (biology), Automotive engine, Automotive engineering, Control (management)
References
- Stable Adaptive Control and Estimation for Nonlinear Systems
- Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
- The Effect of Engine Misfire on Exhaust Emission Levels in Spark Ignition Engines
- Controlling the Sensitivity of Support Vector Machines
- 1.9-Liter Four-Cylinder HCCI Engine Operation with Exhaust Gas Recirculation
- Homogeneous Charge Compression Ignition (HCCI) Using Isooctane, Ethanol and Natural Gas - A Comparison with Spark Ignition Operation
- Control Oriented Model and Dynamometer Testing for a Single-Cylinder, Heated-Air HCCI Engine
- Effect of ambient conditions and fuel properties on homogeneous charge compression ignition engine operation
- A study of gasoline-fuelled HCCI engine equipped with an electromagnetic valve train
- Predictive control : with constraints
- Neural Networks and Learning Machines
- Nonlinear Dynamic Modelling Of Automotive Engines Using Neural Networks
- The Potential of HCCI Combustion for High Efficiency and Low Emissions
- Perturbation signals for system identification
- Isolating the Effects of Fuel Chemistry on Combustion Phasing in an HCCI Engine and the Potential of Fuel Stratification for Ignition Control
- Adaptive neural nets filter using a recursive Levenberg-Marquardt search direction
- On the High Load Limit of Boosted Gasoline HCCI Engine Operating in NVO mode
- A tutorial on support vector regression
- Using exhaust gas recirculation in internal combustion engines: a review
- Efficient Training of Recurrent Neural Network with Time Delays
Cited by
- Real-time occupancy estimation using environmental parameters
- Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines
- A robust safety-oriented autonomous cruise control scheme for electric vehicles based on model predictive control and online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor
- Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system
- Stator winding short-circuit fault diagnosis in induction motors using random forest
- Indoor occupancy estimation using environmental parameters
- Adaptive Machine Learning for Modeling and Control of Non-Stationary, Near Chaotic Combustion in Real-Time.
- A survey on online learning and optimization for spark advance control of SI engines
- Online Nonlinear Dynamic System Identification With Evolving Spatial–Temporal Filters: Case Study on Turbocharged Engine Modeling
- Connected Vehicle Based Distributed Meta-Learning for Online Adaptive Engine/Powertrain Fuel Consumption Modeling
- Deep Learning Based Distributed Meta-Learning for Fast and Accurate Online Adaptive Powertrain Fuel Consumption Modeling
- Special Focus on Learning and Real-time Optimization of Automotive Powertrain Systems A survey on online learning and optimization for spark advance control of SI engines
- Severity Estimation of Stator Winding Short-Circuit Faults Using Cubist
- New Solution Supporting Efficient Vehicle Calibration Using Objective Driveability Evaluation and AI
Related papers
- Support Vector Machines and its Application
- Incremental learning with support vector machines
- Algorithms and Machine Learning
- Machine Learning, Its Limitations, and Solutions Over IT
- Identifying knowledge domain and incremental new class learning in SVM
- Study On Machine Learning Algorithms
- Machine learning algorithms comparison