An innovative design of a dynamic neural network architecture that is able to first learn and then utilize fuzzy-like `IF-THEN' rules is presented in this paper. Each fuzzy neuron in the network represents a compositional rule of inference that defines the relationship between a particular premise and the corresponding consequence. The neural network first determines the similarity between a neural input (a discretely sampled fuzzy set) and the feedforward synaptic weights (accumulated knowledge-base). The `best' definition of the input is selected by competition arising from the dense feedback between the neurons. A satisfactory conclusion is reconstructed in weighted feedforward outputs from the `winning' neuron. The knowledge-base is updated by an unsupervised learning algorithm that adapts the feedforward weights assigned to both the neural inputs and outputs. An example of how this dynamic neural network can be used to perform fuzzy-like inference rules for the navigation of an autonomous vehicle through an unstructured environment is used to illustrate these notions.