You can obtain the equation ( a long one!) when you have the weights and biases. I suggest that you read the manual of  MATLAB neural network and go through through the examples to get a good grasp of ANN. Here is the part from manual:

For multiple-layer networks the layer number determines the superscript on the weight matrix. The appropriate notation is used in the two-layer tansig/purelin network shown next.

The figures from the manuals demonstrate the sort of equations you will obtain.

Dear Shravan Singh Rao,

Technically speaking, computer science researchers usually adopt Genetic Programming approaches to obtain the explicit equation of an response model. In the case of neural networks, however, you can do so. 

http://fr.mathworks.com/help/nnet/ug/multilayer-neural-network-architecture.html

http://www.sciencedirect.com/science/article/pii/S0020025512003295

You can obtain the equation ( a long one!) when you have the weights and biases. I suggest that you read the manual of  MATLAB neural network and go through through the examples to get a good grasp of ANN. Here is the part from manual:

For multiple-layer networks the layer number determines the superscript on the weight matrix. The appropriate notation is used in the two-layer tansig/purelin network shown next.

The figures from the manuals demonstrate the sort of equations you will obtain.

MATLAB codes for training ANN with default MATLAB feedforward network using PSO is available to the following link. I hope it will be useful to you.

https://www.researchgate.net/publication/305325563_Codes_in_MATLAB_for_Training_Artificial_Neural_Network_using_Particle_Swarm_Optimization

Code Codes in MATLAB for Training Artificial Neural Network using...

1- if you need the equation out of MATLAB environment, for multi-layer perception you can extract the equation. but for other networks, i do not know. 

2- if you need the equation inside of MATLAB environment: 

In software like MATLAB, there is a function to calculate outputs from your rained network and there is no need to know weights and biases or other details, to calculate outputs. you should be aware of normalize and denormalize functions.

 If you need outputs inside of MATLAB as an input for another calculations, easily use net(input) to calculate outputs.

Dear Researcher

I think you can find the answer in the attached papers:

1. https://www.researchgate.net/publication/224132638_Modeling_of_forced_dermal_wound_healing_using_intelligent_techniques

2. https://www.researchgate.net/publication/232651566_Modeling_of_Dermal_Wound_Healing-Remodeling_Phase_by_Neural_Networks

also to achieve better result you optimize your ANN by GA, PSO, RPLNN and other optimization techniques :

1. https://www.researchgate.net/publication/306048088_Optimizing_radio_frequency_identification_network_planning_through_ring_probabilistic_logic_neurons

2. https://www.researchgate.net/publication/303402995_Modeling_Mechanical_Properties_of_FSW_Thick_Pure_Copper_Plates_and_Optimizing_It_Utilizing_Artificial_Intelligence_Techniques

3. https://www.researchgate.net/publication/269367058_Introducing_Genetic_Algorithm_as_an_Intelligent_Optimization_Technique

Conference Paper Modeling of forced dermal wound healing using intelligent techniques

Article Modeling of Dermal Wound Healing-Remodeling Phase by Neural Networks

Article Optimizing radio frequency identification network planning t...

Article Modeling Mechanical Properties of FSW Thick Pure Copper Plat...

Article Introducing Genetic Algorithm as an Intelligent Optimization Technique

Dear Researcher

For the model equation using weights in ANN you can use the garson algorithm (1991) or Goh (1995) to determine the relative importance of each input with weights. With the relative importance of each input you can make a nonlinear multivariable regression and have your model.

Good luck

Yassine

I have explained the equation development procedure from a trained ANN model in my paper. Deleted research item The research item mentioned here has been deleted