What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Algorithm
His main research concerns Control theory, Fuzzy control system, Adaptive control, Control system and Control engineering.
His Control theory study frequently intersects with other fields, such as Stability.
Fuzzy control system is a subfield of Fuzzy logic that Kevin M. Passino tackles.
His Adaptive control research incorporates elements of Artificial neural network, Foraging and Biomimetics.
His study on Control system is mostly dedicated to connecting different topics, such as Intelligent control.
His work in Control engineering tackles topics such as Mobile robot which are related to areas like Human–computer interaction.
His most cited work include:
- Biomimicry of bacterial foraging for distributed optimization and control (2332 citations)
- Fuzzy Control (2127 citations)
- Stability analysis of swarms (832 citations)
What are the main themes of his work throughout his whole career to date?
Kevin M. Passino mainly investigates Control theory, Control engineering, Adaptive control, Fuzzy control system and Control theory.
His study in Nonlinear system, Control system, Stability, Nonlinear control and Discrete time and continuous time is done as part of Control theory.
Kevin M. Passino interconnects Expert system, Mobile robot, Fault tolerance, Control and Intelligent control in the investigation of issues within Control engineering.
His research integrates issues of Intelligent decision support system and Real-time Control System in his study of Intelligent control.
His research in Adaptive control intersects with topics in Artificial neural network, Genetic algorithm, Exponential stability and Automatic control.
His Fuzzy control system study necessitates a more in-depth grasp of Fuzzy logic.
He most often published in these fields:
- Control theory (38.39%)
- Control engineering (27.10%)
- Adaptive control (22.90%)
What were the highlights of his more recent work (between 2010-2020)?
- Control theory (38.39%)
- Swarm behaviour (11.29%)
- Mathematical optimization (13.55%)
In recent papers he was focusing on the following fields of study:
Kevin M. Passino mainly focuses on Control theory, Swarm behaviour, Mathematical optimization, Artificial intelligence and Swarm intelligence.
His Control theory study integrates concerns from other disciplines, such as Maximum power point tracking, Grid-connected photovoltaic power system and Control.
Kevin M. Passino has researched Swarm behaviour in several fields, including Swarming, Discrete time and continuous time, Control engineering, Asynchronous communication and Double integrator.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Cognitive science, that overlap with his field of interest in Artificial life and Ethology.
His work in Observer addresses issues such as Kalman filter, which are connected to fields such as Control system.
His Testbed research focuses on Intelligent control and how it relates to Simulation.
Between 2010 and 2020, his most popular works were:
- Swarm Stability and Optimization (101 citations)
- Swarm Cognition: an Interdisciplinary Approach to the study of Self-organising Biological Collectives (54 citations)
- Illumination Balancing Algorithm for Smart Lights (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Algorithm
His primary scientific interests are in Mathematical optimization, Artificial intelligence, Swarm behaviour, Network topology and Equilibrium point.
His Mathematical optimization research includes themes of Algorithm design, Game theory and Group.
His work deals with themes such as Function minimization and Optimal foraging theory, Foraging, which intersect with Artificial intelligence.
His biological study spans a wide range of topics, including Swarming, Direct search, Relevance and Social foraging.
The concepts of his Network topology study are interwoven with issues in Group cohesiveness, Cohesion, Nash equilibrium, Group dynamic and Monte Carlo method.
His Equilibrium point research is multidisciplinary, incorporating perspectives in Node, Replicator equation and Optimization problem.
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