13,593 research outputs found
H^â Optimal Training Algorithms and their Relation to Backpropagation
We derive global H^â optimal training algorithms for neural networks. These algorithms guarantee the smallest possible prediction error energy over all possible disturbances of fixed energy, and are therefore robust with respect to model uncertainties and lack of statistical information on the exogenous signals. The ensuing estimators are infinite-dimensional, in the sense that updating the weight vector estimate requires knowledge of all previous weight esimates. A certain finite-dimensional approximation to these estimators is the backpropagation algorithm. This explains the local H6â optimality of backpropagation that has been previously demonstrated
Backpropagation training in adaptive quantum networks
We introduce a robust, error-tolerant adaptive training algorithm for
generalized learning paradigms in high-dimensional superposed quantum networks,
or \emph{adaptive quantum networks}. The formalized procedure applies standard
backpropagation training across a coherent ensemble of discrete topological
configurations of individual neural networks, each of which is formally merged
into appropriate linear superposition within a predefined, decoherence-free
subspace. Quantum parallelism facilitates simultaneous training and revision of
the system within this coherent state space, resulting in accelerated
convergence to a stable network attractor under consequent iteration of the
implemented backpropagation algorithm. Parallel evolution of linear superposed
networks incorporating backpropagation training provides quantitative,
numerical indications for optimization of both single-neuron activation
functions and optimal reconfiguration of whole-network quantum structure.Comment: Talk presented at "Quantum Structures - 2008", Gdansk, Polan
Parameters Identification for a Composite Piezoelectric Actuator Dynamics
This work presents an approach for identifying the model of a composite piezoelectric (PZT) bimorph actuator dynamics, with the objective of creating a robust model that can be used under various operating conditions. This actuator exhibits nonlinear behavior that can be described using backlash and hysteresis. A linear dynamic model with a damping matrix that incorporates the BoucâWen hysteresis model and the backlash operators is developed. This work proposes identifying the actuatorâs model parameters using the hybrid master-slave genetic algorithm neural network (HGANN). In this algorithm, the neural network exploits the ability of the genetic algorithm to search globally to optimize its structure, weights, biases and transfer functions to perform time series analysis efficiently. A total of nine datasets (cases) representing three different voltage amplitudes excited at three different frequencies are used to train and validate the model. Four cases are considered for training the NN architecture, connection weights, bias weights and learning rules. The remaining five cases are used to validate the model, which produced results that closely match the experimental ones. The analysis shows that damping parameters are inversely proportional to the excitation frequency. This indicates that the suggested hysteresis model is too general for the PZT model in this work. It also suggests that backlash appears only when dynamic forces become dominant
STRATEGY MANAGEMENT IN A MULTI-AGENT SYSTEM USING NEURAL NETWORKS FOR INDUCTIVE AND EXPERIENCE-BASED LEARNING
Intelligent agents and multi-agent systems prove to be a promising paradigm for solving problems in a distributed, cooperative way. Neural networks are a classical solution for ensuring the learning ability of agents. In this paper, we analyse a multi-agent system where agents use different training algorithms and different topologies for their neural networks, which they use to solve classification and regression problems provided by a user. Out of the three training algorithms under investigation, Backpropagation, Quickprop and Rprop, the first demonstrates inferior performance to the other two when considered in isolation. However, by optimizing the strategy of accepting or rejecting tasks, Backpropagation agents succeed in outperforming the other types of agents in terms of the total utility gained. This strategy is learned also with a neural network, by processing the results of past experiences. Therefore, we show a way in which agents can use neural network models for both external purposes and internal ones.agents, learning, neural networks, strategy management multi-agent system.
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning
DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement
learning using deep neural networks. DQNs require a large buffer and batch
processing for an experience replay and rely on a backpropagation based
iterative optimization, making them difficult to be implemented on
resource-limited edge devices. In this paper, we propose a lightweight
on-device reinforcement learning approach for low-cost FPGA devices. It
exploits a recently proposed neural-network based on-device learning approach
that does not rely on the backpropagation method but uses OS-ELM (Online
Sequential Extreme Learning Machine) based training algorithm. In addition, we
propose a combination of L2 regularization and spectral normalization for the
on-device reinforcement learning so that output values of the neural network
can be fit into a certain range and the reinforcement learning becomes stable.
The proposed reinforcement learning approach is designed for PYNQ-Z1 board as a
low-cost FPGA platform. The evaluation results using OpenAI Gym demonstrate
that the proposed algorithm and its FPGA implementation complete a CartPole-v0
task 29.77x and 89.40x faster than a conventional DQN-based approach when the
number of hidden-layer nodes is 64
- âŠ