Sigmoidal fold and fault geometries are typical kinematic indicators of strike-slip zones. We document kilometer-scale, normal faults with sigmoidal plan-view within the dextral pull-apart Bahia Basin, at rear obliquely convergent South Caribbean Deformed Belt, offshore Colombia. Using 3D seismic reflection data calibrated to wells, closely spaced, low-displacement, planar mapped Miocene strata...

In conventional neural networks, we have to define the architecture prior to training but in constructive neural networks the network architecture is constructed during the training process. In this paper, we review constructive neural network algorithms that constructing feedforward architecture for regression problems. Cascade-Correlation algorithm (CCA) is a well-known and widely used constr...

Nonlinear transformation is one of the major obstacles to analyzing the properties of multilayer perceptrons. In this letter, we prove that the correlation coefficient between two jointly Gaussian random variables decreases when each of them is transformed under continuous nonlinear transformations, which can be approximated by piecewise linear functions. When the inputs or the weights of a mul...

The main result of this paper is a (n 1=4) lower bound on the size of a sigmoidal circuit computing a speciic AC 0 2 function. This is the rst lower bound for the computation model of sigmoidal circuits with unbounded weights. We also give upper and lower bounds for the same function in a few other computation models: circuits of AND/OR gates, threshold circuits, and circuits of piecewise-ratio...

Different mathematical models were evaluated as candidates to describe ruminal dry matter (DM) and crude protein (CP) degradation kinetics of raw and roasted whole soybeans from data obtained using the in situ polyester bag technique. Three models were used: segmented with up to 3 straight lines (model I), negative exponential (model II), and rational function or inverse polynomial (linear over...

This paper presents a new approach to the Artificial Neural Networks (ANN) modelling of bacterial growth; using Neural Network models based on Product Units (PUNN) instead of on sigmoidal units (MLP) of kinetic parameters (lag-time, growth rate and maximum population density) of Leuconostoc mesenteroides and those factors affecting their growth such as storage temperature, pH, NaCl and NaNO 2 c...

Although feedforward neural networks are well suited to function approximation , in some applications networks experience problems when learning a desired function. One problem is interference which occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To understand t...