Reservoir Computers Modal Decomposition and Optimization
The topology of a network associated with a reservoir computer is often taken so that the connectivity and the weights are chosen randomly. Optimization is hardly considered as the parameter space is typically too large. Here we investigate this problem for a class of reservoir computers for which we obtain a decomposition of the reservoir dynamics into modes, which can be computed independently of one another. Each mode depends on an eigenvalue of the network adjacency matrix. We then take a parametric approach in which the eigenvalues are parameters that can be appropriately designed and optimized. In addition, we introduce the application of a time shift to each individual mode. We show that manipulations of the individual modes, either in terms of the eigenvalues or the time shifts, can lead to dramatic reductions in the training error.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers
We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization. We use a new measure of reservoir performance, designed to emphasize learnin…
Bayesian OptimizationKernel-based optimization of measurement operators for quantum reservoir computers
Finding optimal measurement operators is crucial for the performance of quantum reservoir computers (QRCs), since they employ a fixed quantum feature map. We formulate the training of both stateless (quantum extreme lear…
Quantum Machine LearningTime Series PredictionImage ClassificationOnline model learning with data-assimilated reservoir computers
We propose an online learning framework for forecasting nonlinear spatio-temporal signals (fields). The method integrates (i) dimensionality reduction, here, a simple proper orthogonal decomposition (POD) projection; (ii…
Dimensionality ReductionState EstimationTime Series ForecastingTuning the activation function to optimize the forecast horizon of a reservoir computer
Reservoir computing is a machine learning framework where the readouts from a nonlinear system (the reservoir) are trained so that the output from the reservoir, when forced with an input signal, reproduces a desired out…
Hyperparameter OptimizationEfficient Optimisation of Physical Reservoir Computers using only a Delayed Input
We present an experimental validation of a recently proposed optimization technique for reservoir computing, using an optoelectronic setup. Reservoir computing is a robust framework for signal processing applications, an…