Embodiments of the present invention provide a method and an apparatus for improving positioning performance of an artificial neural network. The method includes: establishing an initial ANN; optimizing the initial ANN by using a PSO algorithm, to obtain an optimized initial ANN; training the optimized initial ANN to obtain a trained ANN. According to a positioning method and apparatus provided in the embodiments of the present invention, when an ANN is used for positioning, positioning performance is improved by using a PSO algorithm to optimize a weight and an offset of each neural element in the ANN.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for improving positioning performance of an artificial neural network, comprising: obtaining geographical location information and fingerprint information of at least one test point; randomly selecting an initial weight and an initial offset of each neural element at an input layer, an output layer, or a hidden layer by using the fingerprint information of the at least one test point as an input layer neural element and by using the geographical location information of the at least one test point as an output layer neural element, so as to establish an initial artificial neural network (ANN); optimizing the initial weight and the initial offset of each neural element of the initial ANN by using a particle swarm optimization (PSO) algorithm, to obtain an optimized initial ANN; and training the optimized initial ANN to obtain a trained ANN. 2. The method according to claim 1, wherein the optimizing the initial weight and the initial offset of each neural element of the initial ANN by using a PSO algorithm, to obtain an optimized initial ANN comprises: using the initial weight and the initial offset of each neural element of the initial ANN as a particle initial location of the PSO algorithm and randomly selecting an initial speed of each particle, so as to establish a swarm of the PSO algorithm; in each iteration of an iteration process of the PSO algorithm, sequentially computing fitness of each particle in the PSO algorithm, updating an optimal particle in the swarm of the PSO algorithm, and updating a location and a speed of each particle in the swarm of the PSO algorithm, until iteration times of the PSO algorithm end; using a best location of each particle in the swarm of the PSO algorithm as an optimized weight and an optimized offset of each neural element of the initial ANN; and using the optimized weight and the optimized offset of each neural element of the initial ANN to establish the optimized initial ANN. 3. The method according to claim 1, wherein the optimizing the initial weight and the initial offset of each neural element of the initial ANN by using a PSO algorithm, to obtain an optimized initial ANN comprises: using the initial weight and the initial offset of each neural element of the initial ANN as a particle initial location of the PSO algorithm and randomly selecting an initial speed of each particle, so as to establish a swarm of the PSO algorithm; in each iteration of an iteration process of the PSO algorithm, sequentially computing fitness of each particle in the PSO algorithm, updating an optimal particle in the swarm of the PSO algorithm, and updating a location and a speed of each particle in the swarm of the PSO algorithm, until a sum of fitness of all particles in the PSO algorithm is less than a preset threshold; using a best location of each particle in the swarm of the PSO algorithm as an optimized weight and an optimized offset of each neural element of the initial ANN; and using the optimized weight and the optimized offset of each neural element of the initial ANN to establish the optimized initial ANN. 4. The method according to claim 2, wherein the computing fitness of each particle in the PSO algorithm comprises: using the location of each particle in the PSO algorithm as a weight and an offset in the initial ANN; computing an output layer neural element of the initial ANN by using the fingerprint information of the at least one test point as an input layer neural element; and computing an error between the output layer neural element and the geographical location information of the at least one test point, and using the error as the fitness of the particle; and the updating an optimal particle in the swarm of the PSO algorithm comprises: using a minimum-error particle as the optimal particle in the swarm of the PSO algorithm. 5. The method according to claim 1, wherein the ANN is a back propagation-ANN or a generalized regression linear network. 6. The method according to claim 1, wherein the fingerprint information is a received signal strength indicator RSSI. 7. An apparatus for improving positioning performance of an artificial neural network, comprising: a processor; and a non-transitory computer-readable storage medium coupled to the processor and storing programming instructions for execution by the processor, the programming instructions instruct the processor to: obtain geographical location information and fingerprint information of at least one test point; randomly select an initial weight and an initial offset of each neural element at an input layer, an output layer, or a hidden layer by using the fingerprint information of the at least one test point as an input layer neural element and using the geographical location information of the at least one test point as an output layer neural element, so as to establish an initial artificial neural network ANN; optimize the initial weight and the initial offset of each neural element of the initial ANN by using a particle swarm optimization (PSO) algorithm, to obtain an optimized initial ANN; and train the optimized initial ANN to obtain a trained ANN. 8. The apparatus for improving positioning performance of an artificial neural network according to claim 7, wherein the optimize the initial weight and the initial offset of each neural element of the initial ANN by using the PSO algorithm, to obtain an optimized initial ANN comprises: use the initial weight and the initial offset of each neural element of the initial ANN as a particle initial location of the PSO algorithm and randomly select an initial speed of each particle, so as to establish a swarm of the PSO algorithm; in each iteration of an iteration process of the PSO algorithm, sequentially compute fitness of each particle in the PSO algorithm, update an optimal particle in the swarm of the PSO algorithm, and update a location and a speed of each particle in the swarm of the PSO algorithm, until iteration times of the PSO algorithm end; use a best location of each particle in the swarm of the PSO algorithm as an optimized weight and an optimized offset of each neural element of the initial ANN; and use the optimized weight and the optimized offset of each neural element of the initial ANN to establish the optimized initial ANN. 9. The apparatus for improving positioning performance of an artificial neural network according to claim 7, wherein the optimize the initial weight and the initial offset of each neural element of the initial ANN by using the PSO algorithm, to obtain an optimized initial ANN comprises: use the initial weight and the initial offset of each neural element of the initial ANN as a particle initial location of the PSO algorithm and randomly select an initial speed of each particle, so as to establish a swarm of the PSO algorithm; in each iteration of an iteration process of the PSO algorithm, sequentially compute fitness of each particle in the PSO algorithm, update an optimal particle in the swarm of the PSO algorithm, and update a location and a speed of each particle in the swarm of the PSO algorithm, until a sum of fitness of all particles in the PSO algorithm is less than a preset threshold; use a best location of each particle in the swarm of the PSO algorithm as an optimized weight and an optimized offset of each neural element of the initial ANN; and use the optimized weight and the optimized offset of each neural element of the initial ANN to establish the optimized initial ANN. 10. The apparatus for improving positioning performance of an artificial neural network according to claim 8, wherein the compute fitness of each particle in the PSO algorithm comprises: use the location of each particle in the PSO algorithm as a weight and an offset in the initial ANN; compute an output layer neural element of the initial ANN by using the fingerprint information of the at least one test point as an input layer neural element; compute an error between the output layer neural element and the geographical location information of the at least one test point, and use the error as the fitness of the particle; and use a minimum-error particle as the optimal particle in the swarm of the PSO algorithm. 11. The apparatus for improving positioning performance of an artificial neural network according to claim 7, wherein the ANN is a back propagation-ANN or a generalized regression linear network. 12. The apparatus for improving positioning performance of an artificial neural network according to claim 7, wherein the fingerprint information is a received signal strength indicator (RSSI). 13. A non-transitory computer-readable medium comprising instructions, that when executed by a processor, cause the processor to improve position performance of an artificial neural networking by performing operations comprising: obtaining geographical location information and fingerprint information of at least one test point; randomly selecting an initial weight and an initial offset of each neural element at an input layer, an output layer, or a hidden layer by using the fingerprint information of the at least one test point as an input layer neural element and by using the geographical location information of the at least one test point as an output layer neural element, so as to establish an initial artificial neural network (ANN); optimizing the initial weight and the initial offset of each neural element of the initial ANN by using a particle swarm optimization (PSO) algorithm, to obtain an optimized initial ANN; and training the optimized initial ANN to obtain a trained ANN.