CONTRIBUTIONS → TOPICS
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Mathematical and theoretical methods in computational intelligence:
Mathematics for neural networks. RBF structures. Self-organizing networks and methods. Support vector machines and kernel methods. Fuzzy logic. Evolutionary and genetic algorithms. -
Neurocomputational formulations:
Single-neuron modelling. Perceptual modelling. System-level neural modelling. Spiking neurons. Models of biological learning. Learning and adaptation.
Adaptive systems. Imitation learning. Reconfigurable systems. Supervised, non-supervised, reinforcement and statistical algorithms.Emulation of cognitive functions.
Decision Making. Multi-agent systems. Sensor mesh. Natural language. Pattern recognition. Perceptual and motor functions (visual, auditory, tactile, virtual reality, etc.). Robotics. Planning motor control.Bio-inspired systems and neuro-engineering.
Embedded intelligent systems. Evolvable computing. Evolving hardware. Microelectronics for neural, fuzzy and bioinspired systems. Neural prostheses. Retinomorphic systems. Brain-computer interfaces (BCI) Nanosystems. Nanocognitive systems.Hybrid Intelligent Systems.
Soft Computing. Neuro-fuzzy systems. Neuro-evolutionary systems. Neuro-swarm. Hybridizatiion with novel computing paradigms: Qantum computing, DNA computing, membrane computing. Neural dynamic logic and other methods; etc.Applications.
Image and Signal Processing. Ambient intelligence. Biomimetic applications. System identification, process control, and manufacturing. Computational Biology and Bioinformatics. Internet Modeling, Communication and Networking. Intelligent Systems in Education. Human-Robot Interaction. Multi-Agent Systems. Time series analysis and prediction. Data mining and knowledge discovery.
