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A method for estimating the equilibrium capacity of a general class of analog feedback neural networks is presented in this brief paper. Some explicit relationships between upper bound of the number of possible stable equilibria and the network parameters such as self-feedback coefficients, weights, and gains of a feedback neural network are obtained. Increasing the equilibrium capacity using multimodal sigmoidal functions is also discussed. Some examples are provided to demonstrate the effectiveness of the analytical results presented