A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of 'path interference', which makes that the neural net quickly forgets previously learned input-output relations is tackled by adding a Hebbian term (proportional to the learning rate nu) to the reinforcement term (proportional to delta) in the learning rule. It is shown that...

Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of ...

The Adaline network [1] is a classic neural architecture whose learning rule is the famous least mean squares (LMS) algorithm (a.k.a. delta rule or Widrow-Hoff rule). It has been demonstrated that the LMS algorithm is optimal in H∞ sense since it tolerates small (in energy) disturbances, such as measurement noise, parameter drifting and modelling errors [2,3]. Such optimality of the LMS algorit...

The purpose of this study was to develop a leaf-setting algorithm for Dynamic Multileaf Collimator-Intensity-Modulated Radiation Therapy (DMLC-IMRT) for optimal marker visibility. Here, a leaf-setting algorithm (called a Delta algorithm) was developed with the objective of maximizing marker visibility so as to improve the tracking effectiveness of fiducial markers during treatment delivery. The...

Live VM (virtual machine) migration has become a research hotspot of virtualized cloud computing architecture. We present a novel migration algorithm which is called HMDC. Its main idea includes two parts. One is that it combines memory pulling copy with memory pushing copy to achieve hybrid memory copy. The other one is that it uses a delta compression mechanism during dirty pages copy, in whi...

the main challenge of a search engine is ranking web documents to provide the best response to a user`s query. despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. in this paper, a ranking algorithm based on the reinforcement le...

This paper presents a novel technique based on artificial neural networks (ANNs) to correct the line power factor with variable loads. A synchronous motor controlled by the neural compensator was used to handle the reactive power of the system. The ANN compensator was trained with the extended delta-bar-delta learning algorithm. The parameters of the ANN were then inserted into a PIC 16F877 con...

We develop a stochastic approximation-type algorithm to solve finite state/action, infinite-horizon, risk-aware Markov decision processes. Our has two loops. The inner loop computes the risk by solving saddle-point problem. outer performs $Q-$ learning compute an optimal policy. Several widely investigated measures (e.g., conditional value-at-risk, optimized certainty equivalent, and absolute s...