Daniels H, Kamp B, Verkooijen W. 1997. Modeling non-linearity in economic classification with neural networks. International Journal of Intelligent Systems in Accounting, Finance and Management 6: 287-301.Modeling non-linearity in economic classification with neural networks - Daniels, Kamp, et al...
In principle, the second tier constructs a reduced-order space for the non-linearity using the POD algorithm. The neural network is then utilized to predict the coefficients, achieving an affine-decomposable approximation of the non-linearity. The goal of the second tier is hence to approximate ...
We show that the non-linearity in the acoustic transformation plays a critical role in feature extraction. We compute the gain in word success rate provided by a reservoir computing device compared to the acoustic transformation only, and show that it is an appropriate benchmark for comparing ...
Due to the linearity of the evolution, φ(U) is an homomorphism69. This means that, if a given evolution is the sequence of two linear networks W and V, the overall evolution can be written in terms of permanent of submatrices of U = VW. Fig. 1: Non-linear Boson Sampling. a ...
We synthesize the extended linear beta regression with a neural network structure to model and predict the mean and precision of market-based loss rates. We can incorporate non-linearity in mean and precision in a flexible way and resolve the problem of specifying the underlying form in advance....
The proposed method relaxes the linearity in the unknown parameter assumption, separation and certainty equivalence principle and persistency of excitation (PE) condition. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates ...
In this equation, the non-linearity is considered as an additive non-linear term Qf(x(t), u(t),t)), i.e., the system dynamics can be separated into linear and non-linear parts. This kind of non-linear dynamic structure exists in some non-linear chemical processes [17,21,64]. For...
This non-linearity allows for differentiation between fast and slow changes in synaptic signal. The weight update of the system described thus far does not change in time, e.g., between clamped and free states; synapses are always updated with the same fixed local learning rule in Eq. (4)...
In addition, the non-linearity is excessive for large-sized trucks which emit a large amount of exhaust gases. The latter, on the other hand, has a comparatively uniform effect on the pollution and is small in range. In the case where the input information has various dynamic ranges and ...
the activation function defines a complex non-linearity. The activation function for one or more nodes, such as all the nodes with activation functions, are implemented in the complex-value domain. The model for machine learning is defined to include one or more learnable complex-valued activation...