Titlebook: Neurocomputing; Algorithms, Architec Fran?oise Fogelman Soulié,Jeanny Hérault Conference proceedings 1990 Springer-Verlag Berlin Heidelberg

Blood-Clot

繼承人

Product Units with Trainable Exponents and Multi-Layer Networkstead of the output of this unit depending on a weighted sum of the inputs, it depends on a weighted product. In justifying the introduction of a new type of unit we explore at some length the rationale behind the use of multi-layer neural networks, and the properties of the computational units withi

wangle

Optimization of the number of hidden cells in a multilayer perceptron. Validation in the linear casea given task. An incremental algorithm has been proposed [l] which is proven to converge. It essentially works for binary outputs and does not necessarily provide the minimal size solution. We propose another approach, in the general case of continuous neurons.

conformity

Concomitant

Synchronous Boltzmann Machines and Gibbs Fields: Learning Algorithmsem as pattern classifiers that learn, have proposed a learning algorithm for the asynchronous machine. Here we study the synchronous machine where all neurons are simultaneously updated, we compute its equilibrium energy, and propose a synchronous learning algorithm based on . average coactivity of

愛(ài)社交

Learning algorithms in neural networks: recent resultsor perception. The algorithm optimizes the stability of learned patterns, which enlarges the size of the basins of attraction. The second algorithm builds a multilayer feedforward network: it allows one to learn an arbitrary mapping input → output. The convergence of the growth process is guaranteed

拉開(kāi)這車(chē)床

Statistical approach to the Jutten-Hérault algorithmith guidance provided by neurosciences analogies, the unsupervised learning JH algorithm has been adjusted and implemented on an array of p linear neurons totally interconnected [0] [1]. Because of its numerous applications ranging from image processing to antenna array processing, the JH algorithm

IST

Cuisine

Neural Networks Dynamicss (binary states) and also for continuous local rules. By doing so we prove, in the context of binary networks, the convergence to cycles of period one or two for symmetric weights. Also we prove that “almost symmetric” Neural Networks admit large cycles (non-bounded in the size, n, of the array) an

lethargy