# Monotone Multi-Layer Perceptron

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This note is for monotonic Multi-Layer Perceptron Neural network, and the references are from the R package `monmlp`

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## Zhang and Zhang (1999)

The proposed network structure is similar to that of an ordinary multilayer perceptron except that the weights $w_i$ are replaced by $e^{w_i}$.

A single neuron output is defined as

\[z_j = g(a_j)\,, \qquad a_j=b_j+\sum_ie^{w_{ij}}z_i\]It can be shown that $y$ is a always an increasing function of $x$.

## Lang (2005)

A fully connected multi-layer perceptron network (MLP) with $I$ inputs, a first hidden layer with $H$ nodes, a second hidden layer with $L$ nodes and a single output is defined by

\[\begin{align*} \hat y(\bfx) &= w_b + \sum_{l=1}^L w_l\tanh (w_{b,l} + \sum_{h=1}^H \tanh(w_{b,h}+\sum_{i=1}^I w_{hi}x_i))\\ &=w_b + \sum_{l=1}^L w_l\tanh (w_{b,l} + \sum_{h=1}^H \theta_2)\\ &=w_b + \sum_{l=1}^L w_l\theta_1\\ \end{align*}\]MLP ensures a monotonically increasing behavior with respect to the input $x_j\in \bfx$ if

\[\frac{\partial \hat y}{\partial x_j} = \sum_{l=1}^Lw_l\cdot (1-\theta_1^2)\sum_{h=1}^Hw_{lh}(1-\theta_2^2)w_{hj} \ge 0\]The derivative of a hyperbolic tangent is always positive. For this reason, a sufficient condition for a monotonicity increasing behavior for the input dimension $j$ is defined as

\[w_l \cdot w_{lh} \cdot w_{hj} \ge 0 \forall l, h\,.\]## Minin et al. (2010)

Let $R$ denote the number of nodes in the second hidden layer, which equals the number of groups in the first hidden layer. The outputs of the groups are denoted by $g_1,\ldots,g_R$. Let $h_r$ denote the number of hyperplanes within group $r, r=1,2,\ldots, R$. The output at group $r$ is defined by

\[g_r(\bfx) = \min_j(w_{r,j}\cdot x + \theta_{(r, j)}), 1\le j\le h_r\,,\]the final output $\hat y(\bfx)$ of the network for an input $\bfx$ is

\[\hat y(\bfx) = \max_r g_r(\bfx)\,.\]