Subject » BMEVIIIM129
Soft Computing Methods
Lágy számítási módszerek
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Lágy számítási módszerek
Soft Computing Methods
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| Subject code | BMEVIIIM129 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | vizsga | ||||||||||||
| Credits | 4 | ||||||||||||
| Subject coordinator |
DR. Harmati István
position: egyetemi docens
contact:
harmati.istvan@vik.bme.hu
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| Responsible department |
Irányítástechnika és Informatika Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
14 weeks of classes: 26 hours of lectures + 13
hours of classroom practices. Classroom practices illustrate the methods with application
examples. The topics of the lectures are the following:
- Fundamentals of fuzzy-neural
systems. Fuzzy implication, defuzzification, Sugeno-type fuzzy systems.
- The block diagram of Fuzzy Logic
Controllers (FLC), the functionality of the blocks, Fuzzy PID and PD
controllers. MacVicar-Whelan meta
rules. The rule base design of Fuzzy PD controller.
- Overview of numerical
optimization methods. The necessary analytical condition of the optimal
solution considering the constraints. The statement of optimization
problem, aczive set, LICQ condition, the Lagrange function of the
optimization problem. First order (Karush-Kuhn-Tucker) conditions.
- Optimization methods.
Gradient-like, conjugate gradient, quasi Newton methods. Computation of
gradient in neural networks. Subtractive clustering, computation of
gradient in adaptive networks, ANFIS.
- The architecture of Genetic
Algorithms. Linear and nonlinear fitness function, selection, binary and
real genetic operators, reinsertation strategies. Multipopulation
algorithm. Controller design with genetic algorithm.
- Adptive fuzzy control. Nominal
and supervisory control. Indirect (model based) and direct adaptive
control. Stability analysis.
- Direct adaptive neural control
with full state feedback, adaptive control with neural network based
nonlinear observer. Case study: flight control.
- Fuzzy approximation based on
SVD. The algorithm, methods to satisfy the mathematical conditions, multivariable
extension. Control design with SVD
technique.
- Optimization and control design
with evolutionary programming and bacterial algorithms. The algorithms,
guzzy interpretation, control design.
- Swarm intelligence.
Motiovation, common properties, The definition of swarms and
intelliogence. Ant Colony Optimization (ACO). The base of global behavior,
the mathematical model of ants. The difference between the real and
artificial ants. The role of pheromone. The methaheuristic of ACO.
- Particle swarm optimization
(PS). Motiovations, benefits and drawbacks, the concept of PSO, the
artificial swarm, optimization algorithm, the motion of particles.
Implementation issues, discrete implementation, variants.
- Learning algorithms. Algorithms that
learn equilibria, best response. Bounfaries in computations, Wolf
algorithm and its variants. The control of multiagent systems with
learning algorithms.
- Probabilistic model with Bayes
networks.
The goal of the course
is to introduce the state-of-the-art soft computing and artificial intelligence
methods used in system modeling and control theory. The methods are introduced
in the frame of nonlinear identification and control problems.
Students successfully satisfying the course
requirements are prepared in system modeling and to design and implement
control algorithms for complex systems. In general, they are able to contribute
to the solution system optimization and decision making problems. They obtain skills
to apply fuzzy systems, neural networks, genetic algorithms and swarm
intelligence on technological and nontechnological areas (e.g. biology,
economics). Also, they are able to take part in the development and research of
information system with high demand on artificial intelligence techniques.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
26 hours of lectures + 13 hours of classroom practices.
Tanulástámogató anyagok
Online források
[1] Electronic slides on the educational portal: edu.iit.bme.hu; (registration is necessary) ; [2] B. Lantos: Fuzzy systems and genetic algorithms, 2002,; Műegyetemi kiadó
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Mathematics, Control Engineering
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Mathematics, Control Engineering
General rules
Requirements:
One midterm is written during the semester, its result must be at least 2 (on the scale of 1 to 5). The result of midterm gives 20 percent in the result of the finale exam.
Additional possibilities:
The mid-term can be repeated once in the
teaching period.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
-
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.