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A New Formulation for Feedforward Neural Networks
IEEE Transactions on Neural Networks, 2011Feedforward neural network is one of the most commonly used function approximation techniques and has been applied to a wide variety of problems arising from various disciplines. However, neural networks are black-box models having multiple challenges/difficulties associated with training and generalization. This paper initially looks into the internal
Bryan A. Tolson, Saman Razavi
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2021
The feedforward neural network is the most fundamental type of neural networks from historical viewpoint and its wide applicability. This chapter discusses several aspects of this type of neural network in detail. Section 2.1 describes its fundamental structure and algorithm, Sect. 2.2 various types of layers, Sect.
Genki Yagawa, Atsuya Oishi
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The feedforward neural network is the most fundamental type of neural networks from historical viewpoint and its wide applicability. This chapter discusses several aspects of this type of neural network in detail. Section 2.1 describes its fundamental structure and algorithm, Sect. 2.2 various types of layers, Sect.
Genki Yagawa, Atsuya Oishi
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Robustness of feedforward neural networks
IEEE International Conference on Neural Networks, 2002Methods are developed for measuring the sensitivity of links and nodes of a feedforward neural network, and for implementing a technique to ensure the development of neural networks that satisfy well-defined robustness criteria. Experimental observations indicate that performance degradation in the authors' robust feedforward network is significantly ...
Ching-Tai Chiu +3 more
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2020
This chapter provides a more in-depth description of supervised learning, deep learning, and neural networks—presenting the foundational mathematical and statistical learning concepts and explaining how they relate to real-world examples in trading, risk management, and investment management.
Matthew F. Dixon +2 more
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This chapter provides a more in-depth description of supervised learning, deep learning, and neural networks—presenting the foundational mathematical and statistical learning concepts and explaining how they relate to real-world examples in trading, risk management, and investment management.
Matthew F. Dixon +2 more
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Attractor Dynamics in Feedforward Neural Networks
Neural Computation, 2000We study the probabilistic generative models parameterized by feedfor-ward neural networks. An attractor dynamics for probabilistic inference in these models is derived from a mean field approximation for large, layered sigmoidal networks. Fixed points of the dynamics correspond to solutions of the mean field equations, which relate the statistics of ...
Lawrence K. Saul, Michael I. Jordan
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EKF learning for feedforward neural networks
2003 European Control Conference (ECC), 2003Learning for feedforward neural networks can be regarded as a nonlinear parameter estimation problem with the objective of finding the optimal weights that provide the best fitting of a given training set. The extended Kalman filter is well-suited to accomplishing this task, as it is a recursive state estimation method for nonlinear systems.
ALESSANDRI, ANGELO +4 more
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Function evaluation with feedforward neural networks
International Journal of Computer Mathematics, 1998Much effort has previously been spent in investigating the decision making/object identification capabilities of feedforward neural networks. In the present work we examine the less frequently investigated abilities of such networks to implement computationally useful operations in arithmetic and function evaluation.
Doug Logan, Panos Argyrakis
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2018
In Chapter 2, we did some amazing things with one neuron, but that is hardly flexible enough to tackle more complex cases. The real power of neural networks comes to light when several (thousand, even million) neurons interact with each other to solve a specific problem. The network architecture (how neurons are connected to each other, how they behave,
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In Chapter 2, we did some amazing things with one neuron, but that is hardly flexible enough to tackle more complex cases. The real power of neural networks comes to light when several (thousand, even million) neurons interact with each other to solve a specific problem. The network architecture (how neurons are connected to each other, how they behave,
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Functionally Equivalent Feedforward Neural Networks
Neural Computation, 1994For a feedforward perceptron type architecture with a single hidden layer but with a quite general activation function, we characterize the relation between pairs of weight vectors determining networks with the same input-output function.
Vera Kurková, Paul C. Kainen
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