In the course of present research, we examined a method to control the environment of a cognitive radio using a PNN neural network as a decision-making system. As a result of research into the WRAN environment control architecture using a neural network, a flow chart of the environment control algorithm has been developed. Its special feature is that a neural network is located at each base station and interacts with other WRANs according to the IEEE 802.22 standard. The cognitive radio environment control architecture has been improved using a PNN network. This is achieved by applying a special case of radial basis networks ‒ a probabilistic neural network and a hybrid learning system, as well as a hybrid form of error correction and accumulating the experience of past iterations. To simulate a PNN neural network, the MATLAB software package was selected using standard functions of "Neural" and "Simulink" sections. To determine the two measurable vectors of the input set, four domains of input vectors with a normal distribution law with arbitrary values have been created. As a result of the network simulation, a connectivity matrix corresponding to the input vector has been generated. A PNN neural network simulation showed statistically confirmed results. The network has one competing layer and a layer for receiving and splitting the attributes of the input vector. This ensures the use of a small number of network neurons and, accordingly, the fast learning ability of the network – 1200 ms, which is 1.67 times faster than the required value, which is achieved by employing parallel processing of information. Moreover, the improved method provides the ability to work in the presence of a large number of uninformative, noise input signals, as well as the adaptation to environmental changes