In this paper we propose adaptive brain-inspired emotional decayed learning to predict Kp, AE and Dst indices that characterize the chaotic activity of the earth's magnetosphere by their extreme lows and highs. In mammalian brain, the limbic system processes emotional stimulus and consists of two main components: Amygdala and Orbitofrontal Cortex (OFC). Here, we propose a learning algorithm for...

In this paper, we propose a novel Takagi-SugenoKang type interval-valued neural fuzzy system with asymmetric fuzzy membership functions (called TIVNFS-A). In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. Based on the Lyapunov stability theorem, the TIVNFS-A system is trained by the back-propag...

Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural networks. RMSProp offers efficient training since it uses first order gradients to approximate Hessianbased preconditioning. However, since the first order gradients include noise caused by stochastic optimization, the approximation may be inaccurate. In this paper, we propose a novel adaptive learning ra...

First, this paper recalls a recently introduced method of adaptive monitoring of dynamical systems and presents the most recent extension with a multiscale-enhanced approach. Then, it is shown that this concept of real-time data monitoring establishes a novel non-Shannon and non-probabilistic concept of novelty quantification, i.e., Entropy of Learning, or in short the Learning Entropy. This no...

Stochastic (on-line) learning can be faster than batch learning. However, at late times, the learning rate must be annealed to remove the noise present in the stochastic weight updates. In this annealing phase, the convergence rate (in mean square) is at best proportional to l/T where T is the number of input presentations. An alternative is to increase the batch size to remove the noise. In th...

In training a back-propagation neural network, the learning speed of the network is greatly affected by its learning rate. None, however, has offered a deterministic method for selecting the optimal learning rate. Some researchers have tried to find the sub-optimal learning rates using various techniques at each training step. This paper proposes a new method for selecting the sub-optimal learn...

The basic idea of this paper is to increase the learning rate of a artificial neural network without affecting the accuracy of the system. The new algorithms for dynamically reducing the number of input samples presented to the ANN (Artificial Neural Network) are given thus increasing the rate of learning. This method is called as Adaptive skipping. This can be used along with any supervised Le...

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the corr...

Most of the performance bounds for on-line learning algorithms are proven assuming a constant learning rate. To optimize these bounds, the learning rate must be tuned based on quantities that are generally unknown, as they depend on the whole sequence of examples. In this paper we show that essentially the same optimized bounds can be obtained when the algorithms adaptively tune their learning ...