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Evolutionary fuzzy system for architecture control in a constructive neural network
An evolutionary system to control the growth of a constructive neural network for autonomous navigation is described and the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture is shown.
Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing
This contribution shows that the recently emerged paradigm of Reservoir Computing (RC) is very well suited to solve all of the above mentioned problems, namely learning by example, robot localization, map and path generation.
Physics-Informed Neural Nets-based Control
This work presents a new framework called Physics-Informed Neural Nets-based Control (PINC), which proposes a novel PINN-based architecture that is amenable to control problems and able to simulate for longer-range time horizons that are not fixed beforehand.
Event detection and localization for small mobile robots using reservoir computing
Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear
Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells
This work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model simulation of a vertical riser model; and another for estimating the down hole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements.
On Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures
A general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments and three learning approaches for navigation behaviors are shown.
Online learning control with Echo State Networks of an oil production platform
This work contributes to the literature by demonstrating that online-learning control can be effective in highly complex dynamic systems (oil production platforms) devoid of suitable models, and with multiple inputs and outputs.
Nonlinear Model Predictive Control of an Oil Well with Echo State Networks
An efficient data-driven framework for Model Predictive Control (MPC) using Echo State Networks (ESN) as the prediction model is proposed, by using the analytically computed gradient from the ESN model, a finite difference method is not needed to compute derivatives as in PNMPC.
Event Detection and Localization in Mobile Robot Navigation Using Reservoir Computing
This work uses a randomly created recurrent neural network where only a linear readout layer is trained, used for detecting complex events in autonomous robot navigation and can be extended to robot localization based solely on sensory information.