Abstract
In this short paper we report on an inverse problem for parameter setting of a model used for the modelling of fishing on the West African coast. We compare the solution of this inverse problem by a Neural Network with the more classical algorithms of optimisation and stochastic control. The Neural Network does much better.
Highlights
We consider a coastal area with a single fishing zone
The mortality due to fishing is represented by a Schaefer function which is proportional to fish biomass and fishing effort E (t )
In case of all identical boats, the fishing effort is proportional to the total number of boats of the fishing fleet
Summary
We consider a coastal area with a single fishing zone. The model introduced in [1] is summarised below. We assume that the fish population grows according to a logistic equation with a positive growth rate r and a carrying capacity K. The fishing effort is a function of the price of fish at time t , p(t ):. This equation says that the rate of the fishing effort is proportional to the difference between the revenue and the cost of fishing per unit of fishing effort. The cost per unit of fishing effort c is a constant which incorporates the fuel for the boats, the taxes to pay to the government, the minimal revenue wanted by boat owners as well as the wages for the fishermen.
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