10 research outputs found
An algebraic framework to represent finite state machines in single-layer recurrent neural networks
In this paper we present an algebraic framework to represent finite state machines (FSMs) in single-layer recurrent neural networks (SLRNNs), which unifies and generalizes some of the previous proposals. This framework is based on the formulation of both the state transition function and the output function of an FSM as a linear system of equations, and it permits an analytical explanation of the representational capabilities of first-order and higher-order SLRNNs. The framework can be used to insert symbolic knowledge in RNNs prior to learning from examples and to keep this knowledge while training the network. This approach is valid for a wide range of activation functions, whenever some stability conditions are met. The framework has already been used in practice in a hybrid method for grammatical inference reported elsewhere (Sanfeliu and Alquézar 1994).Peer Reviewe
Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture
Real-time state-of-health monitoring of lithium-ion battery with anomaly detection, Levenberg–Marquardt algorithm, and multiphase exponential regression model
Prognostics of lithium-ion batteries based on different dimensional state equations in the particle filtering method
High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression
State of health prediction for lithium-ion batteries using multiple-view feature fusion and support vector regression ensemble
Accelerating the discovery of materials for clean energy in the era of smart automation
The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace