2.4k citations · 2.6k across the 9 of their papers we have counts for
9 papers
Augmented neural forms with parametric boundary-matching operators for solving ordinary differential equations
Adam D. Kypriadis, Isaac E. Lagaris, Aristidis Likas +1
Approximating solutions of ordinary and partial differential equations constitutes a significant challenge. Based on functional expressions that inherently depend on neural network…
Global -means: an effective relaxation of the global -means clustering algorithm
Georgios Vardakas, Aristidis Likas
The -means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial posi…
A Set Membership Approach to Discovering Feature Relevance and Explaining Neural Classifier Decisions
Stavros P. Adam, Aristidis C. Likas
Neural classifiers are non linear systems providing decisions on the classes of patterns, for a given problem they have learned. The output computed by a classifier for each patter…
The UU-test for Statistical Modeling of Unimodal Data
Paraskevi Chasani, Aristidis Likas
Deciding on the unimodality of a dataset is an important problem in data analysis and statistical modeling. It allows to obtain knowledge about the structure of the dataset, ie. wh…
A Replication Strategy for Mobile Opportunistic Networks based on Utility Clustering
Evangelos Papapetrou, Aristidis Likas
Dynamic replication is a wide-spread multi-copy routing approach for efficiently coping with the intermittent connectivity in mobile opportunistic networks. According to it, a node…
Neural Network Methods for Boundary Value Problems Defined in Arbitrarily Shaped Domains
I. E. Lagaris, A. Likas, D. G. Papageorgiou
Partial differential equations (PDEs) with Dirichlet boundary conditions defined on boundaries with simple geometry have been succesfuly treated using sigmoidal multilayer perceptr…
Training Reinforcement Neurocontrollers Using the Polytope Algorithm
A. Likas, I. E. Lagaris
A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorit…
Artificial Neural Networks for Solving Ordinary and Partial Differential Equations
I. E. Lagaris, A. Likas, D. I. Fotiadis
We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. T…
Artificial Neural Network Methods in Quantum Mechanics
I. E. Lagaris, A. Likas, D. I. Fotiadis
In a previous article we have shown how one can employ Artificial Neural Networks (ANNs) in order to solve non-homogeneous ordinary and partial differential equations. In the prese…