5.1Hopfieldneuralnetwork In1982,HopfieldJJbroughtforwardasinglelayerfeedbacknetworkcomposedofnonlinearcomponent.Feedbacknetworkisanonlineardynamicsystem.•NonlineardifferenceequationDiscreteHNN(DHNN)•differentialequationContinuousHNN(CHNN)Applications:associativememoryorclassificationcomputingforoptimization 5.1.1Discrete...
Each time a neural network is trained, can result in a different solution due to different initial weight and bias values and different divisions of data into training, validation, and test sets. As a result, different neural networks trained on the same problem can give different outputs for ...
First results from off-line tests suggest the potential of implementing new operational autoscaling software in the worldwide Digisonde network. 展开 关键词: Artificial neural networks Rotors Atmospheric modeling Robustness Earth Atmospheric waves Meteorology ...
In this blog post we explore the differences between deed-forward and feedback neural networks, look at CNNs and RNNs, examine popular examples of Neural Net…
It is shown that all the signals in the closed-loop system are uniformly ultimately bounded. No off-line learning phase is needed; initialization of the network weights is straightforward. 展开 关键词: Theoretical or Mathematical/ closed loop systems continuous time systems feedforward neural nets ...
Neural networks in the brain are dominated by sometimes more than 60% feedback connections, which most often have small synaptic weights. Different from this, little is known how to introduce feedback into artificial neural networks. Here we use transfer entropy in the feed-forward paths of deep...
This problem is addressed by training an artificial neural network to represent the simulation output. This approach is demonstrated on the simulated extrusion of a plain carbon steel rod 展开 关键词: extrusion materials processing metal forming output feedback process control ...
A novel neural network design, in which nonlinearities are created by feedback, is described. It is called the HOFNET. The design is suitable for optical implementation because it is tolerant of the limited dynamic ranges present in optical systems. An optical system with electronic feedback was...
A neural net model-based multivariable long-range predictive control strategy applied in thermal power plant control A constrained multivariable control strategy along with its application in more efficient thermal power plant control is presented in this paper. A neural network model-based nonlinear ...
A robust neural network output feedback scheme is developed for the motion control of robot manipulators without measuring joint velocities. A neural network observer is presented to estimate the joint velocities. It is shown that all the signals in a closed-loop system composed of a robot, an ...